Techniques and methods for storing and transferring registration, atlas, and lead information between medical devices

Kothandaraman , et al. March 28, 2

Patent Grant 9604067

U.S. patent number 9,604,067 [Application Number 13/957,353] was granted by the patent office on 2017-03-28 for techniques and methods for storing and transferring registration, atlas, and lead information between medical devices. This patent grant is currently assigned to Boston Scientific Neuromodulation Corporation. The grantee listed for this patent is BOSTON SCIENTIFIC NEUROMODULATION CORPORATION. Invention is credited to Sridhar Kothandaraman, Michael A. Moffitt.


United States Patent 9,604,067
Kothandaraman ,   et al. March 28, 2017

Techniques and methods for storing and transferring registration, atlas, and lead information between medical devices

Abstract

A neurostimulator system includes a portable component configured for storing patient-specific data, and an external control device configured for obtaining the patient-specific data from the portable component. The portable component is an implantable neurostimulator, a patient's remote controller, and/or an external charger. The patient-specific data is imaging-related data. A method of storing data in a neurostimulation system includes generating patient-specific data, and storing the patient-specific data in at least one of the portable components. A method for programming the implantable neurostimulator includes receiving the patient-specific data from the portable component, simulating a volume of tissue activation for each of one or more candidate stimulation parameters, wherein the simulation is based at least in part on the patient-specific data, selecting at least one of the candidate stimulation parameters, and programming the implantable neurostimulator with the selected stimulation parameters.


Inventors: Kothandaraman; Sridhar (Valencia, CA), Moffitt; Michael A. (Valencia, CA)
Applicant:
Name City State Country Type

BOSTON SCIENTIFIC NEUROMODULATION CORPORATION

Valencia

CA

US
Assignee: Boston Scientific Neuromodulation Corporation (Valencia, CA)
Family ID: 49029179
Appl. No.: 13/957,353
Filed: August 1, 2013

Prior Publication Data

Document Identifier Publication Date
US 20140039577 A1 Feb 6, 2014

Related U.S. Patent Documents

Application Number Filing Date Patent Number Issue Date
61679717 Aug 4, 2012

Current U.S. Class: 1/1
Current CPC Class: A61N 1/37235 (20130101); A61N 1/0534 (20130101); A61B 2090/364 (20160201); A61N 1/36062 (20170801); A61B 2034/107 (20160201); A61N 1/0529 (20130101); A61B 34/10 (20160201); A61B 34/20 (20160201)
Current International Class: A61N 1/372 (20060101); A61N 1/36 (20060101); A61B 34/20 (20160101); A61B 34/10 (20160101); A61B 90/00 (20160101); A61N 1/05 (20060101)
Field of Search: ;607/45,46,59,60

References Cited [Referenced By]

U.S. Patent Documents
3999555 December 1976 Person
4144889 March 1979 Tyers et al.
4177818 December 1979 De Pedro
4341221 July 1982 Testerman
4378797 April 1983 Osterholm
4445500 May 1984 Osterholm
4735208 April 1988 Wyler et al.
4765341 August 1988 Mower et al.
4841973 June 1989 Stecker
5067495 November 1991 Brehm
5099846 March 1992 Hardy
5222494 June 1993 Baker, Jr.
5255693 October 1993 Dutcher
5259387 November 1993 dePinto
5304206 April 1994 Baker, Jr. et al.
5344438 September 1994 Testerman et al.
5361763 November 1994 Kao et al.
5452407 September 1995 Crook
5560360 October 1996 Filler et al.
5565949 October 1996 Kasha, Jr.
5593427 January 1997 Gliner et al.
5601612 February 1997 Gliner et al.
5607454 March 1997 Cameron et al.
5620470 April 1997 Gliner et al.
5651767 July 1997 Schulmann
5711316 January 1998 Elsberry et al.
5713922 February 1998 King
5716377 February 1998 Rise et al.
5724985 March 1998 Snell et al.
5749904 May 1998 Gliner et al.
5749905 May 1998 Gliner et al.
5776170 July 1998 MacDonald et al.
5782762 July 1998 Vining
5843148 December 1998 Gijsbers et al.
5859922 January 1999 Hoffmann
5868740 February 1999 LeVeen et al.
5897583 April 1999 Meyer et al.
5910804 June 1999 Fortenbery et al.
5925070 July 1999 King et al.
5938688 August 1999 Schiff
5938690 August 1999 Law et al.
5978713 November 1999 Prutchi et al.
6016449 January 2000 Fischell et al.
6029090 February 2000 Herbst
6029091 February 2000 de la Rama et al.
6050992 April 2000 Nichols
6058331 May 2000 King
6066163 May 2000 John
6083162 July 2000 Vining
6094598 July 2000 Elsberry et al.
6096756 August 2000 Crain et al.
6106460 August 2000 Panescu et al.
6109269 August 2000 Rise et al.
6128538 October 2000 Fischell et al.
6129685 October 2000 Howard, III
6146390 November 2000 Heilbrun et al.
6161044 December 2000 Silverstone
6167311 December 2000 Rezai
6181969 January 2001 Gord
6192266 February 2001 Dupree et al.
6205361 March 2001 Kuzma
6208881 March 2001 Champeau
6240308 May 2001 Hardy et al.
6246912 June 2001 Sluijter et al.
6253109 June 2001 Gielen
6289239 September 2001 Panescu et al.
6301492 October 2001 Zonenshayn
6310619 October 2001 Rice
6319241 November 2001 King
6336899 January 2002 Yamazaki
6343226 January 2002 Sunde et al.
6351675 February 2002 Tholen et al.
6353762 March 2002 Baudino et al.
6366813 April 2002 Dilorenzo
6368331 April 2002 Front et al.
6389311 May 2002 Whayne et al.
6393325 May 2002 Mann et al.
6421566 July 2002 Holsheimer
6435878 August 2002 Reynolds et al.
6442432 August 2002 Lee
6463328 October 2002 John
6491699 December 2002 Henderson et al.
6494831 December 2002 Koritzinsky
6507759 January 2003 Prutchi et al.
6510347 January 2003 Borkan
6516227 February 2003 Meadows et al.
6517480 February 2003 Krass
6539263 March 2003 Schiff
6560490 May 2003 Grill et al.
6579280 June 2003 Kovach et al.
6600956 July 2003 Maschino et al.
6606523 August 2003 Jenkins
6609029 August 2003 Mann et al.
6609031 August 2003 Law et al.
6609032 August 2003 Woods et al.
6622048 September 2003 Mann et al.
6631297 October 2003 Mo
6654642 November 2003 North et al.
6662053 December 2003 Borkan
6675046 January 2004 Holsheimer
6684106 January 2004 Herbst
6687392 February 2004 Touzawa et al.
6690972 February 2004 Conley et al.
6690974 February 2004 Archer et al.
6692315 February 2004 Soumillion et al.
6694162 February 2004 Hartlep
6694163 February 2004 Vining
6708096 March 2004 Frei et al.
6741892 May 2004 Meadows et al.
6748098 June 2004 Rosenfeld
6748276 June 2004 Daignault, Jr. et al.
6778846 August 2004 Martinez et al.
6788969 September 2004 Dupree et al.
6795737 September 2004 Gielen et al.
6827681 December 2004 Tanner et al.
6830544 December 2004 Tanner
6845267 January 2005 Harrison et al.
6850802 February 2005 Holsheimer
6895280 May 2005 Meadows et al.
6909913 June 2005 Vining
6937891 August 2005 Leinders et al.
6937903 August 2005 Schuler et al.
6944497 September 2005 Stypulkowski
6944501 September 2005 Pless
6950707 September 2005 Whitehurst
6969388 November 2005 Goldman et al.
7003349 February 2006 Andersson et al.
7003352 February 2006 Whitehurst
7008370 March 2006 Tanner et al.
7008413 March 2006 Kovach et al.
7035690 April 2006 Goetz
7043293 May 2006 Baura
7047082 May 2006 Schrom et al.
7047084 May 2006 Erickson et al.
7050857 May 2006 Samuelsson et al.
7054692 May 2006 Whitehurst et al.
7058446 June 2006 Schuler et al.
7082333 July 2006 Bauhahn et al.
7107102 September 2006 Daignault et al.
7127297 October 2006 Law et al.
7136518 November 2006 Griffin et al.
7136695 November 2006 Pless et al.
7142923 November 2006 North et al.
7146219 December 2006 Sieracki et al.
7146223 December 2006 King
7151961 December 2006 Whitehurst
7155279 December 2006 Whitehurst
7167760 January 2007 Dawant et al.
7177674 February 2007 Echauz et al.
7181286 February 2007 Sieracki et al.
7184837 February 2007 Goetz
7191014 March 2007 Kobayashi et al.
7209787 April 2007 Dilorenzo
7211050 May 2007 Caplygin
7216000 May 2007 Sieracki et al.
7217276 May 2007 Henderson
7218968 May 2007 Condie et al.
7228179 June 2007 Campen et al.
7231254 June 2007 DiLorenzo
7236830 June 2007 Gliner
7239910 July 2007 Tanner
7239916 July 2007 Thompson et al.
7239926 July 2007 Goetz
7242984 July 2007 DiLorenzo
7244150 July 2007 Brase et al.
7252090 August 2007 Goetz
7254445 August 2007 Law et al.
7254446 August 2007 Erickson
7257447 August 2007 Cates et al.
7266412 September 2007 Stypulkowski
7294107 November 2007 Simon et al.
7295876 November 2007 Erickson
7299096 November 2007 Balzer et al.
7308302 December 2007 Schuler et al.
7313430 December 2007 Urquhart
7324851 January 2008 DiLorenzo
7346382 March 2008 McIntyre et al.
7388974 June 2008 Yanagita
7437193 October 2008 Parramon et al.
7463928 December 2008 Lee et al.
7499048 March 2009 Sieracki et al.
7505815 March 2009 Lee et al.
7548786 June 2009 Lee et al.
7565199 July 2009 Sheffield et al.
7603177 October 2009 Sieracki et al.
7617002 November 2009 Goetz
7623918 November 2009 Goetz
7650184 January 2010 Walter
7657319 February 2010 Goetz et al.
7672734 March 2010 Anderson et al.
7676273 March 2010 Goetz et al.
7680526 March 2010 McIntyre et al.
7734340 June 2010 De Ridder
7761165 July 2010 He et al.
7826902 November 2010 Stone et al.
7848802 December 2010 Goetz et al.
7860548 December 2010 McIntyre et al.
7904134 March 2011 McIntyre et al.
7945105 May 2011 Jaenisch
7949395 May 2011 Kuzma
7974706 July 2011 Moffitt et al.
8019439 September 2011 Kuzma et al.
8175710 May 2012 He
8180601 May 2012 Butson et al.
8195300 June 2012 Gliner et al.
8224450 July 2012 Brase
8257684 September 2012 Covalin et al.
8262714 September 2012 Hulvershorn et al.
8364278 January 2013 Pianca et al.
8429174 April 2013 Ramani et al.
8452415 May 2013 Goetz et al.
8543189 September 2013 Paitel et al.
8606360 December 2013 Butson et al.
8620452 December 2013 King et al.
8918184 December 2014 Torgerson et al.
2001/0031071 October 2001 Nichols et al.
2002/0032375 March 2002 Bauch et al.
2002/0062143 May 2002 Baudino et al.
2002/0087201 July 2002 Firlik et al.
2002/0099295 July 2002 Gil et al.
2002/0115603 August 2002 Whitehouse
2002/0116030 August 2002 Rezei
2002/0123780 September 2002 Grill et al.
2002/0128694 September 2002 Holsheimer
2002/0151939 October 2002 Rezai
2002/0183607 December 2002 Bauch et al.
2002/0183740 December 2002 Edwards et al.
2002/0183817 December 2002 Van Venrooij et al.
2003/0097159 May 2003 Schiff et al.
2003/0149450 August 2003 Mayberg
2003/0171791 September 2003 KenKnight et al.
2003/0212439 November 2003 Schuler et al.
2004/0034394 February 2004 Woods et al.
2004/0044279 March 2004 Lewin et al.
2004/0044378 March 2004 Holsheimer
2004/0044379 March 2004 Holsheimer
2004/0054297 March 2004 Wingeier et al.
2004/0059395 March 2004 North et al.
2004/0106916 June 2004 Quaid et al.
2004/0133248 July 2004 Frei et al.
2004/0152957 August 2004 Stivoric et al.
2004/0181262 September 2004 Bauhahn
2004/0186532 September 2004 Tadlock
2004/0199216 October 2004 Lee et al.
2004/0267330 December 2004 Lee et al.
2005/0021090 January 2005 Schuler et al.
2005/0033380 February 2005 Tanner et al.
2005/0049649 March 2005 Luders et al.
2005/0060001 March 2005 Singhal et al.
2005/0060009 March 2005 Goetz
2005/0070781 March 2005 Dawant et al.
2005/0075689 April 2005 Toy et al.
2005/0085714 April 2005 Foley et al.
2005/0165294 July 2005 Weiss
2005/0171587 August 2005 Daglow et al.
2005/0228250 October 2005 Bitter et al.
2005/0251061 November 2005 Schuler et al.
2005/0261061 November 2005 Nguyen et al.
2005/0261601 November 2005 Schuler et al.
2005/0261747 November 2005 Schuler et al.
2005/0267347 December 2005 Oster
2005/0288732 December 2005 Schuler et al.
2006/0004422 January 2006 De Ridder
2006/0017749 January 2006 McIntyre et al.
2006/0020292 January 2006 Goetz et al.
2006/0069415 March 2006 Cameron et al.
2006/0094951 May 2006 Dean et al.
2006/0095088 May 2006 De Riddler
2006/0155340 July 2006 Schuler et al.
2006/0206169 September 2006 Schuler
2006/0218007 September 2006 Bjorner et al.
2006/0224189 October 2006 Schuler et al.
2006/0235472 October 2006 Goetz et al.
2006/0259079 November 2006 King
2006/0259099 November 2006 Goetz et al.
2007/0000372 January 2007 Rezai et al.
2007/0017749 January 2007 Dold et al.
2007/0027514 February 2007 Gerber
2007/0043268 February 2007 Russell
2007/0049817 March 2007 Preiss et al.
2007/0067003 March 2007 Sanchez et al.
2007/0078498 April 2007 Rezai et al.
2007/0083104 April 2007 Butson et al.
2007/0123953 May 2007 Lee et al.
2007/0129769 June 2007 Bourget et al.
2007/0135855 June 2007 Foshee et al.
2007/0150036 June 2007 Anderson
2007/0156186 July 2007 Lee et al.
2007/0162086 July 2007 DiLorenzo
2007/0162235 July 2007 Zhan et al.
2007/0168004 July 2007 Walter
2007/0168007 July 2007 Kuzma et al.
2007/0185544 August 2007 Dawant et al.
2007/0191887 August 2007 Schuler et al.
2007/0191912 August 2007 Ficher et al.
2007/0197891 August 2007 Shachar et al.
2007/0203450 August 2007 Berry
2007/0203532 August 2007 Tass et al.
2007/0203537 August 2007 Goetz et al.
2007/0203538 August 2007 Stone et al.
2007/0203539 August 2007 Stone et al.
2007/0203540 August 2007 Goetz et al.
2007/0203541 August 2007 Goetz et al.
2007/0203543 August 2007 Stone et al.
2007/0203544 August 2007 Goetz et al.
2007/0203545 August 2007 Stone et al.
2007/0203546 August 2007 Stone et al.
2007/0213789 September 2007 Nolan et al.
2007/0213790 September 2007 Nolan et al.
2007/0244519 October 2007 Keacher et al.
2007/0245318 October 2007 Goetz et al.
2007/0255321 November 2007 Gerber et al.
2007/0255322 November 2007 Gerber et al.
2007/0265664 November 2007 Gerber et al.
2007/0276441 November 2007 Goetz
2007/0282189 December 2007 Dan et al.
2007/0288064 December 2007 Butson et al.
2008/0027346 January 2008 Litt
2008/0027514 January 2008 DeMulling et al.
2008/0039895 February 2008 Fowler et al.
2008/0071150 March 2008 Miesel et al.
2008/0081982 April 2008 Simon et al.
2008/0086451 April 2008 Torres et al.
2008/0103533 May 2008 Patel et al.
2008/0114233 May 2008 McIntyre et al.
2008/0114579 May 2008 McIntyre et al.
2008/0123922 May 2008 Gielen et al.
2008/0123923 May 2008 Gielen et al.
2008/0133141 June 2008 Frost
2008/0141217 June 2008 Goetz et al.
2008/0154340 June 2008 Goetz et al.
2008/0154341 June 2008 McIntyre et al.
2008/0163097 July 2008 Goetz et al.
2008/0183256 July 2008 Keacher
2008/0188734 August 2008 Suryanarayanan et al.
2008/0215118 September 2008 Goetz et al.
2008/0227139 September 2008 Deisseroth et al.
2008/0242950 October 2008 Jung et al.
2008/0261165 October 2008 Steingart et al.
2008/0269588 October 2008 Csavoy et al.
2008/0300654 December 2008 Lambert et al.
2008/0300797 December 2008 Tabibiazar et al.
2009/0016491 January 2009 Li
2009/0054950 February 2009 Stephens
2009/0082640 March 2009 Kovach et al.
2009/0082829 March 2009 Panken et al.
2009/0112289 April 2009 Lee et al.
2009/0118635 May 2009 Lujan et al.
2009/0118786 May 2009 Meadows et al.
2009/0149917 June 2009 Whitehurst et al.
2009/0196471 August 2009 Goetz et al.
2009/0196472 August 2009 Goetz et al.
2009/0198306 August 2009 Goetz et al.
2009/0198354 August 2009 Wilson
2009/0204192 August 2009 Carlton et al.
2009/0208073 August 2009 McIntyre et al.
2009/0210208 August 2009 McIntyre et al.
2009/0220136 September 2009 Bova
2009/0242399 October 2009 Kamath et al.
2009/0276008 November 2009 Lee et al.
2009/0281595 November 2009 King et al.
2009/0281596 November 2009 King et al.
2009/0287271 November 2009 Blum et al.
2009/0287272 November 2009 Kokones et al.
2009/0287273 November 2009 Carlton et al.
2009/0287467 November 2009 Sparks et al.
2009/0299164 December 2009 Singhal et al.
2009/0299165 December 2009 Singhal et al.
2009/0299380 December 2009 Singhal et al.
2010/0010566 January 2010 Thacker et al.
2010/0010646 January 2010 Drew et al.
2010/0023103 January 2010 Elborno
2010/0023130 January 2010 Henry et al.
2010/0030312 February 2010 Shen
2010/0049276 February 2010 Blum et al.
2010/0049280 February 2010 Goetz
2010/0064249 March 2010 Groetken
2010/0113959 May 2010 Pascual-Leon et al.
2010/0121409 May 2010 Kothandaraman et al.
2010/0135553 June 2010 Joglekar
2010/0137944 June 2010 Zhu
2010/0152604 June 2010 Kuala et al.
2010/0179562 July 2010 Linker et al.
2010/0324410 December 2010 Paek et al.
2010/0331883 December 2010 Schmitz et al.
2011/0040351 February 2011 Buston et al.
2011/0066407 March 2011 Butson et al.
2011/0172737 July 2011 Davis et al.
2011/0184487 July 2011 Alberts et al.
2011/0191275 August 2011 Lujan et al.
2011/0196253 August 2011 McIntyre et al.
2011/0213440 September 2011 Fowler et al.
2011/0306845 December 2011 Osorio
2011/0306846 December 2011 Osorio
2011/0307032 December 2011 Goetz et al.
2012/0027272 February 2012 Akinyemi et al.
2012/0046715 February 2012 Moffitt et al.
2012/0078106 March 2012 Dentinger et al.
2012/0089205 April 2012 Boyden et al.
2012/0116476 May 2012 Kothandaraman
2012/0165898 June 2012 Moffitt
2012/0165901 June 2012 Zhu et al.
2012/0207378 August 2012 Gupta et al.
2012/0226138 September 2012 DeSalles et al.
2012/0229468 September 2012 Lee et al.
2012/0265262 October 2012 Osorio
2012/0265268 October 2012 Blum et al.
2012/0265271 October 2012 Goetz
2012/0302912 November 2012 Moffitt et al.
2012/0303087 November 2012 Moffitt et al.
2012/0314924 December 2012 Carlton et al.
2012/0316619 December 2012 Goetz et al.
2013/0030276 January 2013 McIntyre
2013/0039550 February 2013 Blum et al.
2013/0060305 March 2013 Bokil
2013/0116748 May 2013 Bokil et al.
2013/0116749 May 2013 Carlton et al.
2013/0116929 May 2013 Carlton et al.
2014/0067018 March 2014 Carcieri et al.
2014/0277284 September 2014 Chen et al.
2015/0134031 May 2015 Moffitt et al.
Foreign Patent Documents
1048320 Nov 2000 EP
1166819 Jan 2002 EP
1372780 Jan 2004 EP
1559369 Aug 2005 EP
97/39797 Oct 1997 WO
98/48880 Nov 1998 WO
01/90876 Nov 2001 WO
02/26314 Apr 2002 WO
02/28473 Apr 2002 WO
02/065896 Aug 2002 WO
02/072192 Sep 2002 WO
03/086185 Oct 2003 WO
2004/019799 Mar 2004 WO
2004041080 May 2004 WO
WO 2004041080 May 2004 WO
2006017053 Feb 2006 WO
2006113305 Oct 2006 WO
2007097859 Aug 2007 WO
2007097860 Aug 2007 WO
2007097861 Aug 2007 WO
2007/100427 Sep 2007 WO
2007/100428 Sep 2007 WO
2007/112061 Oct 2007 WO
2009097224 Aug 2009 WO
2010/ 120823 Oct 2010 WO
2011025865 Mar 2011 WO
2011/139779 Nov 2011 WO
2011/159688 Dec 2011 WO
2012088482 Jun 2012 WO

Other References

Hunka, K. et al., Nursing Time to Program and Assess Deep Brain Stimulators in Movement Disorder Patients, J. Neursci Nurs., 37: 204-10 (Aug. 2005). cited by applicant .
PCT International Search Report for PCT/US2013/053311, Applicant: Boston Scientific Neuromodulation Corporation, Form PCT/ISA/210 and 220, dated Nov. 11, 2013 (4pages). cited by applicant .
PCT Written Opinion of the International Search Authority for PCT/US2013/053311, Applicant: Boston Scientific Neuromodulation Corporation, Form PCT/ISA/237, dated Nov. 11, 2013 (4pages). cited by applicant .
Nowinski, W. L., et al., "Statistical analysis of 168 bilateral subthalamic nucleus implantations by means of the probabilistic functional atlas.", Neurosurgery 57(4 Suppl) (Oct. 2005),319-30. cited by applicant .
Obeso, J. A., et al., "Deep-brain stimulation of the subthalamic nucleus or the pars interna of the globus pallidus in Parkinson's disease.", N Engl J Med., 345{13I. The Deep-Brain Stimulation for Parkinson's Disease Study Group, (Sep. 27, 2001 ),956-63. cited by applicant .
Butson et al.. "Current Steering to control the volume of tissue activated during deep brain stimulation," vol. 1, No. 1, Dec. 3, 2007, pp. 7-15. cited by applicant .
Patrick, S. K., et al., "Quantification of the UPDRS rigidity scale", IEEE Transactions on Neural Systems and Rehabilitation Engineering, [see also IEEE Trans. on Rehabilitation Engineering 9(1). (2001),31-41. cited by applicant .
Phillips, M. D., et al., "Parkinson disease: pattern of functional MR imaging activation during deep brain stimulation of subthalamic nucleus--initial experience", Radiology 239(1). (Apr. 2006),209-16. cited by applicant .
Ericsson, A. et al., "Construction of a patient-specific atlas of the brain: Application to normal aging," Biomedical Imaging: From Nano to Macro, ISBI 2008, 5th IEEE International Symposium, May 14, 2008, pp. 480-483. cited by applicant .
Kaikai Shen et al., "Atlas selection strategy using least angle regression in multi-atlas segmentation propagation," Biomedical Imaging: From Nano to Macro, 2011, 8th IEEE International Symposium, ISBI 2011, Mar. 30, 2011, pp. 1746-1749. cited by applicant .
Liliane Ramus et al., "Assessing selection methods in the cotnext of multi-atlas based segmentation," Biomedical Imaging: From Nano to Macro, 2010, IEEE International Symposium, Apr. 14, 2010, pp. 1321-1324. cited by applicant .
Olivier Commowick et al., "Using Frankenstein's Creature Paradigm to Build a Patient Specific Atlas," Sep. 20, 2009, Medical Image Computing and Computer-Assisted Intervention, pp. 993-1000. cited by applicant .
Lotjonen J.M.P. et al., "Fast and robust multi-atlas segmentation of brain magnetic resonance images," NeuroImage, Academic Press, vol. 49, No. 3, Feb. 1, 2010, pp. 2352-2365. cited by applicant .
McIntyre, C. C., et al., "How does deep brain stimulation work? Present understanding and future questions.", J Clin Neurophysiol. 21 (1 ). (Jan.-Feb. 2004 ),40-50. cited by applicant .
Sanchez Castro et al., "A cross validation study of deep brain stimulation targeting: From experts to Atlas-Based, Segmentation-Based and Automatic Registration Algorithms," IEEE Transactions on Medical Imaging, vol. 25, No. 11, Nov. 1, 2006, pp. 1440-1450. cited by applicant .
Plaha, P. , et al., "Stimulation of the caudal zona incerta is superior to stimulation of the subthalamic nucleus in improving contralateral parkinsonism.", Brain 129{Pt 7) (Jul. 2006), 1732-4 7. cited by applicant .
Rattay, F, "Analysis of models for external stimulation of axons", IEEE Trans. Biomed. Eng. vol. 33 (1986),974-977. cited by applicant .
Rattay, F., "Analysis of the electrical excitation of CNS neurons", IEEE Transactions on Biomedical Engineering 45 (6). (Jun. 1998),766-772. cited by applicant .
Rose, T. L., et al., "Electrical stimulation with Pt electrodes. VIII. Electrochemically safe charge injection limits with 0.2 ms pulses [neuronal application]", IEEE Transactions on Biomedical Engineering, 37(11 }, (Nov. 1990), 1118-1120. cited by applicant .
Rubinstein, J. T., et al., "Signal coding in cochlear implants: exploiting stochastic effects of electrical stimulation", Ann Otol Rhinol Laryngol Suppl.. 191, (Sep. 2003), 14-9. cited by applicant .
Schwan, H.P., et al., "The conductivity of living tissues.", Ann NY Acad Sci., 65(6). (AUQ., 1957), 1007-13. cited by applicant .
Taylor, R. S., et al., "Spinal cord stimulation for chronic back and leg pain and failed back surgery syndrome: a systematic review and analysis of prognostic factors", Spine 30(1 ). (Jan. 1, 2005), 152-60. cited by applicant .
Siegel, Ralph M. et al., "Spatiotemporal dynamics of the functional architecture for gain fields in inferior parietal lobule of behaving monkey," Cerebral Cortex, New York, NY, vol. 17, No. 2, Feb. 2007, pp. 378-390. cited by applicant .
Klein, A. et al., "Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration," NeuroImage, Academic Press, Orlando, FL, vol. 46, No. 3, Jul. 2009, pp. 786-802. cited by applicant .
Geddes, L. A., et al., "The specific resistance of biological material--a compendium of data for the biomedical engineer and physiologist.", Med Biol Ena. 5(3). (May 1967),271-93. cited by applicant .
Gimsa, J., et al., "Choosing electrodes for deep brain stimulation experiments--electrochemical considerations.", J Neurosci Methods, 142(2), (Mar. 30, 2005),251-65. cited by applicant .
Vidailhet, M. , et al., "Bilateral deep-brain stimulation of the globus pallidus in primary generalized dystonia", N Engl J Med. 352(5) (Feb. 3, 2005),459-67. cited by applicant .
Izad, Oliver, "Computationally Efficient Method in Predicating Axonal Excitation," Dissertation for Master Degree, Department of Biomedical Engineering, Case Western Reserve University, May 2009. cited by applicant .
Jaccard, Paul, "Elude comparative de la distribution florale dans une portion odes Aples et des Jura," Bulletin de la Societe Vaudoise des Sciences Naturelles (1901), 37:547-579. cited by applicant .
Dice, Lee R., "Measures of the Amount of Ecologic Association Between Species," Ecology 26(3) (1945): 297-302. doi:10.2307/ 1932409, http://jstor.org/stable/1932409. cited by applicant .
Rand, WM., "Objective criteria for the evaluation of clustering methods," Journal of the American Statistical Association (American Statistical Association) 66 (336) (1971 ): 846-850, doi:10.2307/2284239, http://jstor.org/stable/2284239. cited by applicant .
Hubert, Lawrence et al., "Comparing partitions," Journal of Classification 2(1) (1985): 193-218, doi:10.1007/BF01908075. cited by applicant .
Cover, T.M. et al., "Elements of information theory," (1991) John Wiley & Sons, New York, NY. cited by applicant .
Meila, Marina, "Comparing Clusterings by the Variation of Information," Learning Theory and Kernel Machines (2003): 173-187. cited by applicant .
Viola, P., et al., "Alignment by maximization of mutual information", International Journal of Com outer Vision 24(2). ( 1997), 137-154. cited by applicant .
Butson et al. "StimExplorer: Deep Brain Stimulation Parameter Selection Software System," Acta Neurochirugica, Jan. 1, 2007, vol. 97, No. 2, pp. 569-574. cited by applicant .
Butson et al. "Role of Electrode Design on the Volume of Tissue Activated During Deep Brain Stimulation," Journal of Neural Engineering, Mar. 1, 2006, vol. 3, No. 1, pp. 1-8. cited by applicant .
Volkmann et al., Indroduction to the Programming of Deep Brain Stimulators, Movement Disorders, vol. 17, Suppl. 3, pp. S181-S187 (2002). cited by applicant .
Miocinovic et al. "Cicerone: Stereotactic Neurophysiological Recording and Deep Brain Stimulation Electrode Placement Software System," Acta Neurochirurgica Suppl., Jan. 1, 2007, vol. 97, No. 2, pp. 561-567. cited by applicant .
Schmidt et al. "Sketching and Composing Widgets for 3D Manipulation," Eurographics, Apr. 2008, vol. 27, No. 2, pp. 301-310. cited by applicant .
Volkmann, J. , et al., "Basic algorithms for the programming of deep brain stimulation in Parkinson's disease", Mov Disord., 21 Suppl 14. (Jun. 2006),S284-9. cited by applicant .
Walter, B. L., et al., "Surgical treatment for Parkinson's disease", Lancet Neural. 3(12). (Dec. 2004),719-28. cited by applicant .
Wei, X. F., et al., "Current density distributions, field distributions and impedance analysis of segmented deep brain stimulation electrodes", J Neural Eng .. 2(4). (Dec. 2005), 139-47. cited by applicant .
Zonenshayn, M. , et al., "Location of the active contact within the subthalamic nucleus (STN) in the treatment of idiopathic Parkinson's disease.", Surg Neurol., 62(3) (Sep. 2004),216-25. cited by applicant .
Da Silva et al (A primer on diffusion tensor imaging of anatomical substructures. Neurosurg Focus 15(1): p. 1-4, Article 4, 2003.). cited by applicant .
Micheli-Tzanakou, E., et al., "Computational Intelligence for target assesment in Parkinson's disease", Proceedings of SPIE vol. 4479. Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation IV,(2001),54-69. cited by applicant .
Grill, W. M., "Stimulus waveforms for selective neural stimulation", IEEE Engineering in Medicine and Biology Magazine, 14(4}, (Jul.-Aug. 1995), 375-385. cited by applicant .
Miocinovic, S., et al., "Sensitivity of temporal excitation properties to the neuronal element activated by extracellular stimulation", J Neurosci Methods. 132(1). (Jan. 15, 2004), 91-9. cited by applicant .
Grill, W. M., et al., "Deep brain stimulation creates an informational lesion of the stimulated nucleus", Neuroreport. 15I7t (May 19, 2004), 1137-40. cited by applicant .
Moss, J. , et al., "Electron microscopy of tissue adherent to explanted electrodes in dystonia and Parkinson's disease", Brain, 127{Pt 12). (Dec. 2004 ),2755-63. cited by applicant .
Montgomery, E. B., et al., "Mechanisms of deep brain stimulation and future technical developments.", Neurol Res. 22(3). (Apr. 2000),259-66. cited by applicant .
Merrill, D. R., et al., "Electrical stimulation of excitable tissue: design of efficacious and safe protocols", J Neurosci Methods. 141(2), (Feb. 15, 2005), 171-98. cited by applicant .
Fisekovic et al., "New Controller for Functional Electrical Stimulation Systems", Med. Eng. Phys. 2001; 23:391-399. cited by applicant .
Zhang, Y., et al., "Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy," Neuroimage 52(4) (2010), pp. 1289-1301. cited by applicant .
""BioPSE" The Biomedical Problem Solving Environment", htt12://www.sci.utah.edu/cibc/software/index.html, MCRR Center for Integrative Biomedical Computing,(2004). cited by applicant .
Andrews, R. J., "Neuroprotection trek--the next generation: neuromodulation I. Techniques--deep brain stimulation, vagus nerve stimulation, and transcranial magnetic stimulation.", Ann NY Acad Sci. 993. (May 2003),1-13. cited by applicant .
Carnevale, N.T. et al., "The Neuron Book," Cambridge, UK: Cambridge University Press (2006), 480 pages. cited by applicant .
Chaturvedi: "Development of Accurate Computational Models for Patient-Specific Deep Brain Stimulation," Electronic Thesis or Dissertation, Jan. 2012, 162 pages. cited by applicant .
Chaturvedi, A. et al.: "Patient-specific models of deep brain stimulation: Influence of field model complexity on neural activation predictions." Brain Stimulation, Elsevier, Amsterdam, NL, vol. 3, No. 2 Apr. 2010, pp. 65-77. cited by applicant .
Frankemolle, et al., "Reversing cognitive-motor impairments in Parkinson's disease patients using a computational modeling approach to deep brain stimulation programming," Brian 133 (2010), pp. 746-761. cited by applicant .
McIntyre, C.C., et al., "Modeling the excitablitity of mammalian nerve fibers: influence of afterpotentials on the recovery cycle," J Neurophysiol, 87(2) (Feb. 2002), pp. 995-1006. cited by applicant .
Peterson, et al., "Predicting myelinated axon activation using spatial characteristics of the extracellular field," Journal of Neural Engineering, 8 (2011), 12 pages. cited by applicant .
Warman, et al., "Modeling the Effects of Electric Fields on nerver Fibers; Dermination of Excitation Thresholds,"IEEE Transactions on Biomedical Engineering, vol. 39, No. 12 (Dec. 1992), pp. 1244-1254. cited by applicant .
Wesselink, et al., "Analysis of Current Density and Related Parameters in Spinal Cord Stimulation," IEEE Transactions on Rehabilitation Engineering, vol. 6, No. 2 Jun. 1998, pp. 200-207. cited by applicant .
Andrews, R. J., "Neuroprotection trek--the next generation: neuromodulation II. Applications--epilepsy, nerve regeneration, neurotrophins.", Ann NY Acad Sci. 993 (May 2003), 14-24. cited by applicant .
Astrom, M. , et al., "The effect of cystic cavities on deep brain stimulation in the basal ganglia: a simulation-based study", J Neural Eng., 3(2), (Jun. 2006).132-8. cited by applicant .
Bazin et al., "Free Software Tools for Atlas-based Volumetric Neuroimage Analysis", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, 1824 May 5, 2005. cited by applicant .
Back, C. , et al., "Postoperative Monitoring of the Electrical Properties of Tissue and Electrodes in Deep Brain Stimulation", Neuromodulation, 6(4), (Oct. 2003 ),248-253. cited by applicant .
Baker, K. B., et al., "Evaluation of specific absorption rate as a dosimeter of MRI-related implant heating", J Magn Reson Imaging., 20(2), (Aug. 2004),315-20. cited by applicant .
Brown, J. "Motor Cortex Stimulation," Neurosurgical Focus ( Sep. 15, 2001) 11(3):E5. cited by applicant .
Budai et al., "Endogenous Opioid Peptides Acting at m-Opioid Receptors in the Dorsal Horn Contribute to Midbrain Modulation of Spinal Nociceptive Neurons," Journal of Neurophysiology (1998) 79(2): 677-687. cited by applicant .
Cesselin, F. "Opioid and anti-opioid peptides," Fundamental and Clinical Pharmacology (1995) 9(5): 409-33 (Abstract only). cited by applicant .
Rezai et al., "Deep Brain Stimulation for Chronic Pain" Surgical Management of Pain, Chapter 44 pp. 565-576 (2002). cited by applicant .
Xu, MD., Shi-Ang, article entitled "Comparison of Half-Band and Full-Band Electrodes for Intracochlear Electrical Stimulation", Annals of Otology, Rhinology & Laryngology (Annals of Head & Neck Medicine & Surgery), vol. 102 (5) pp. 363-367 May 1993. cited by applicant .
Bedard, C. , et al., "Modeling extracellular field potentials and the frequency-filtering properties of extracellular space", Biophys J .. 86(3). (Mar. 2004),1829-42. cited by applicant .
Benabid, A. L., et al., "Future prospects of brain stimulation", Neurol Res.;22(3), (Apr. 2000),237-46. cited by applicant .
Brummer, S. B., et al., "Electrical Stimulation with Pt Electrodes: II--Estimation of Maximum Surface Redox (Theoretical Non-Gassing) Limits", IEEE Transactions on Biomedical Engineering, vol. BME-24, Issue 5, (Sep. 1977),440-443. cited by applicant .
Butson, Christopher R., et al., "Deep Brain Stimulation of the Subthalamic Nucleus: Model-Based Analysis of the Effects of Electrode Capacitance on the Volume of Activation", Proceedings of the 2nd International IEEE EMBS, (Mar. 16-19, 2005),196-197. cited by applicant .
Mcintyre, Cameron C., et al., "Cellular effects of deep brain stimulation: model-based analysis of activation and inhibition," J Neurophysiol, 91(4) (Apr. 2004), pp. 1457-1469. cited by applicant .
Chaturvedi, A., et al., "Subthalamic Nucleus Deep Brain Stimulation: Accurate Axonal Threshold Prediction with Diffusion Tensor Based Electric Field Models", Engineering in Medicine and Biology Society, 2006. EMBS' 06 28th Annual International Conference of the IEEE, IEEE, Piscataway, NJ USA, Aug. 30, 2006. cited by applicant .
Butson, Christopher et al., "Predicting the Effects of Deep Brain Stimulation with Diffusion Tensor Based Electric Field Models" Jan. 1, 2001, Medical Image Computing and Computer-Assisted Intervention--Mic CAI 2006 Lecture Notes in Computer Science; LNCS, Springer, Berlin, DE. cited by applicant .
Butson, C. R., et al., "Deep brainstimulation interactive visualization system", Society for Neuroscience vol. 898.7 (2005). cited by applicant .
Hodaie, M., et al., "Chronic anterior thalamus stimulation for intractable epilepsy," Epilepsia, 43(6) (Jun. 2002), pp. 603-608. cited by applicant .
Hoekema, R., et al., "Multigrid solution of the potential field in modeling electrical nerve stimulation," Comput Biomed Res., 31(5) (Oct. 1998), pp. 348-362. cited by applicant .
Holsheimer, J., et al., "Identification of the target neuronal elements in electrical deep brain stimulation," Eur J Neurosci., 12(12) (Dec. 2000), pp. 4573-4577. cited by applicant .
Jezernik, S., et al., "Neural network classification of nerve activity recorded in a mixed nerve," Neurol Res., 23(5) (Jul. 2001), pp. 429-434. cited by applicant .
Jones, DK., et al., "Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging," Magn. Reson. Med., 42(3) (Sep. 1999), pp. 515-525. cited by applicant .
Krack, P., et al., "Postoperative management of subthalamic nucleus stimulation for Parkinson's disease," Mov. Disord., vol. 17(suppl 3) (2002), pp. 188-197. cited by applicant .
Le Bihan, D., et al., "Diffusion tensor imaging: concepts and applications," J Magn Reson Imaging, 13(4) (Apr. 2001), pp. 534-546. cited by applicant .
Lee, D. C., et al., "Extracellular electrical stimulation of central neurons: quantitative studies," In: Handbook of neuroprosthetic methods, WE Finn and PG Lopresti (eds) CRC Press (2003), pp. 95-125. cited by applicant .
Levy, AL., et al., "An Internet-connected, patient-specific, deformable brain atlas integrated into a surgical navigation system," J Digit Imaging, 10(3 Suppl 1) (Aug. 1997), pp. 231-237. cited by applicant .
Liu, Haiying, et al., "Intra-operative MR-guided DBS implantation for treating PD and ET," Proceedings of SPIE vol. 4319, Department of Radiology & Neurosurgery, University of Minnesota, Minneapolis, MN 55455 (2001), pp. 272-276. cited by applicant .
Mcintyre, C. C., et al., "Extracellular stimulation of central neurons: influence of stimulus waveform and frequency on neuronal output," J. Neurophysiol., 88(4), (Oct. 2002), pp. 1592-1604. cited by applicant .
Mcintyre, C. C., et al., "Microstimulation of spinal motoneurons: a model study," Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology society, vol. 5, (1997), pp. 2032-2034. cited by applicant .
Mcintyre, Cameron C., et al., "Model-based Analysis of deep brain stimulation of the thalamus," Proceedings of the Second joint EMBS/BM ES Conference, vol. 3, Annual Fall Meeting of the Biomedical Engineering Society (Cal. No. 02CH37392) IEEEPiscataway, NJ (2002), pp. 2047-2048. cited by applicant .
Mcintyre, C. C., et al., "Model-based design of stimulus trains for selective microstimulation of targeted neuronal populations," Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1 (2001), pp. 806-809. cited by applicant .
Mcintyre, C. C., et al., Model-based design of stimulus waveforms for selective microstimulation in the central nervous system,, Proceedings of the First Joint [Engineering in Medicine and Biology, 1999. 21st Annual Conf. and the 1999 Annual FallMeeting of the Biomedical Engineering Soc.] BM ES/EMBS Conference, vol. 1 (1999), p. 384. cited by applicant .
Mcintyre, Cameron C., et al., "Modeling the excitability of mammalian nerve fibers: influence of aflerpotentials on the recovery cycle," J Neurophysiol, 87(2) (Feb. 2002), pp. 995-1006. cited by applicant .
Mcintyre, Cameron C., et al., "Selective microstimulation of central nervous system neurons," Annals of biomedical engineering, 28(3) (Mar. 2000), pp. 219-233. cited by applicant .
Mcintyre, C. C., et al., "Sensitivity analysis of a model of mammalian neural membrane," Biol Cybern., 79(1) (Jul. 1998), pp. 29-37. cited by applicant .
Mcintyre, Cameron C., et al., "Uncovering the mechanism(s) of action of deep brain stimulation: activation, inhibition, or both," Clin Neurophysiol, 115(6) (Jun. 2004), pp. 1239-1248. cited by applicant .
Mcintyre, Cameron C., et al., "Uncovering the mechanisms of deep brain stimulation for Parkinson's disease through functional imaging, neural recording, and neural modeling," Crit Rev Biomed Eng., 30(4-6) (2002), pp. 249-281. cited by applicant .
Mouine et al. "Multi-Strategy and Multi-Algorithm Cochlear Prostheses", Biomed. Sci. Instrument, 2000; 36:233-238. cited by applicant .
Mcintyre, Cameron C., et al., "Electric Field and Stimulating Influence generated by Deep Brain Stimulation of the Subthalamaic Nucleus," Clinical Neurophysiology, 115(3) (Mar. 2004), pp. 589-595. cited by applicant .
Mcintyre, Cameron C., et al., "Electric field generated by deep brain stimulation of the subthalamic nucleus," Biomedical Engineering Society Annual Meeting, Nashville TN (Oct. 2003), 16 pages. cited by applicant .
Mcintyre, Cameron C., et al., "Excitation of central nervous system neurons by nonuniform electric fields," Biophys. J., 76(2) (1999), pp. 878-888. cited by applicant .
McNeal, DR., et al., "Analysis of a model for excitation of myelinated nerve," IEEE Trans Biomed Eng., vol. 23 (1976), pp. 329-337. cited by applicant .
Micheli-Tzanakou, E. , et al., "Computational Intelligence for target assesment in Parkinson's disease," Proceedings of SPIE vol. 4479, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation IV (2001 ), pp. 54-69. cited by applicant .
Miocinovic, S., et al., "Computational analysis of subthalamic nucleus and lenticular fasciculus activation during therapeutic deep brain stimulation," J Neurophysiol., 96(3) (Sep. 2006), pp. 1569-1580. cited by applicant .
Miranda, P. C., et al., "The distribution of currents inducedin the brain by Magnetic Stimulation: a finite element analysis incorporating OT-MRI-derived conductivity data," Proc. Intl. Soc. Mag. Reson. Med. 9 (2001 ), p. 1540. cited by applicant .
Miranda, P. C., et al., "The Electric Field Induced in the Brain by Magnetic Stimulation: A 3-D Finite-Element Analysis of the Effect of Tissue Heterogeneity and Anisotropy," IEEE Transactions on Biomedical Enginering, 50(9) (Sep. 2003), pp. 1074-1085. cited by applicant .
Moffitt, MA., et al., "Prediction of myelinated nerve fiber stimulation thresholds: limitations of linear models," IEEE Transactions on Biomedical Engineering, 51 (2) (2003), pp. 229-236. cited by applicant .
Moro, E, et al., "The impact on Parkinson's disease of electrical parameter settings in STN stimulation," Neurology, 59 (5) (Sep. 10, 2002), pp. 706-713. cited by applicant .
Nowak, LG., et al., "Axons, but not cell bodies, are activated by electrical stimulation in cortical gray matter. I. Evidence from chronaxie measurements," Exp. Brain Res., 118(4) (Feb. 1998), pp. 477-488. cited by applicant .
Nowak, LG., et al., "Axons, but not cell bodies, are activated by electrical stimulation in cortical gray matter. II. Evidence from selective inactivation of cell bodies and axon initial segments," Exp. Brain Res., 118(4) (Feb. 1998), pp. 489-500. cited by applicant .
O'Suilleabhain, PE., et al., "Tremor response to polarity, voltage, pulsewidth and frequency of thalamic stimulation," Neurology, 60(5) (Mar. 11, 2003), pp. 786-790. cited by applicant .
Pierpaoli, C., et al., "Toward a quantitative assessment of diffusion anisotropy," Magn Reson Med., 36(6) (Dec. 1996), pp. 893-906. cited by applicant .
Plonsey, R., et al., "Considerations of quasi-stationarity in electrophysiological systems," Bull Math Biophys., 29(4) (Dec. 1967), pp. 657-664. cited by applicant .
Ranck, J B., "Specific impedance of rabbit cerebral cortex," Exp. Neurol., vol. 7 (Feb. 1963), pp. 144-152. cited by applicant .
Ranck, J B., et al., "The Specific impedance of the dorsal columns of the cat: an anisotropic medium," Exp. Neurol., 11 (Apr. 1965), pp. 451-463. cited by applicant .
Ranck, J B., "Which elements are excited in electrical stimulation of mammalian central nervous system: a review," Brain Res., 98(3) (Nov. 21, 1975), pp. 417-440. cited by applicant .
Rattay, F., et al., "A model of the electrically excited human cochlear neuron. I. Contribution of neural substructures to the generation and propagation of spikes," Hear Res., 153(1-2) (Mar. 2001), pp. 43-63. cited by applicant .
Rattay, F., "A model of the electrically excited human cochlear neuron. II. Influence of the three-dimensional cochlear structure on neural excitability," Hear Res., 153(1-2) (Mar. 2001), pp. 64-79. cited by applicant .
Rattay, F., "Arrival at Functional Electrostimulation by modelling of fiber excitation," Proceedings of the Ninth annual Conference of the IEEE Engineering in Medicine and Biology Society (1987), pp. 1459-1460. cited by applicant .
Rattay, F., "The inftuence of intrinsic noise can preserve the temporal fine structure of speech signals in models of electrically stimulated human cochlear neurones," Journal of Physiology, Scientific Meeting of the Physiological Society, London, England, UK Apr. 19-21, 1999 (Jul. 1999), p. 170P. cited by applicant .
Rizzone, M., et al., "Deep brain stimulation of the subthalamic nucleus in Parkinson's disease: effects of variation in stimulation parameters," J. Neurol. Neurosurg. Psychiatry., 71(2) (Aug. 2001), pp. 215-219. cited by applicant .
Saint-Cyr, J. A., et al., "Localization of clinically effective stimulating electrodes in the human subthalamic nucleus on magnetic resonance imaging," J. Neurosurg., 87(5) (Nov. 2002), pp. 1152-1166. cited by applicant .
Sances, A., et al., "In Electroanesthesia: Biomedical and Biophysical Studies," A Sances and SJ Larson, Eds., Academic Press, NY (1975), pp. 114-124. cited by applicant .
SI. Jean, P., et al., "Automated atlas integration and interactive three-dimensional visualization tools for planning and guidance in functional neurosurgery," IEEE Transactions on Medical Imaging, 17(5) (1998), pp. 672-680. cited by applicant .
Starr, P.A., et al., "Implantation of deep brain stimulators into the subthalamic nucleus: technical approach and magnetic resonance imaging-verified lead locations," J. Neurosurg., 97(2) (Aug. 2002), pp. 370-387. cited by applicant .
Sterio, D., et al., "Neurophysiological refinement of subthalamic nucleus targeting," Neurosurgery, 50(1) (Jan. 2002), pp. 58-69. cited by applicant .
Struijk, J. J., et al., "Excitation of dorsal root fibers in spinal cord stimulation: a theoretical study," IEEE Transactions on Biomedical Engineering, 40(7) (Jul. 1993), pp. 632-639. cited by applicant .
Struijk, J J., et al., "Recruitment of dorsal column fibers in spinal cord stimulation: inftuence of collateral branching," IEEE Transactions on Biomedical Engineering, 39(9) (Sep. 1992), pp. 903-912. cited by applicant .
Tamma, F., et al., "Anatomo-clinical correlation of intraoperative stimulation-induced side-effects during HF-DBS of the subthalamic nucleus," Neurol Sci., vol. 23 (Suppl 2) (2002), pp. 109-110. cited by applicant .
Tarler, M., et al., "Comparison between monopolar and tripolar configurations in chronically implanted nerve cuff electrodes," IEEE 17th Annual Conference Engineering in Medicine and Biology Society, vol. 2 (1995), pp. 1093-1109. cited by applicant .
Testerman, Roy L., "Coritical response to callosal stimulation: A model for determining safe and efficient stimulus parameters," Annals of Biomedical Engineering, 6(4) (1978), pp. 438-452. cited by applicant .
Tuch, D.S., et al., "Conductivity mapping of biological tissue using diffusion MRI," Ann NY Acad Sci., 888 (Oct. 30, 1999), pp. 314-316. cited by applicant .
Tuch, D.S., et al., "Conductivity tensor mapping of the human brain using diffusion tensor MRI," Proc Nall Acad Sci USA, 98(20) (Sep. 25, 2001), pp. 11697-11701. cited by applicant .
Veraart, C., et al., "Selective control of muscle activation with a multipolar nerve cuff electrode," IEEE Transactions on Biomedical Engineering, 40(7) (Jul. 1993), pp. 640-653. cited by applicant .
Vercueil, L., et al., "Deep brain stimulation in the treatment of severe dystonia," J. Neurol., 248(8) (Aug. 2001 ), pp. 695-700. cited by applicant .
Vilalte, "Circuit Design of the Power-on-Reset," Apr. 2000, pp. 1-25. cited by applicant .
Vitek, J. L., "Mechanisms of deep brain stimulation: excitation or inhibition," Mov. Disord., vol. 17 (Suppl. 3) (2002), pp. 69-72. cited by applicant .
Voges, J., et al., "Bilateral high-frequency stimulation in the subthalamic nucleus for the treatment of Parkinson disease: correlation of therapeutic effect with anatomical electrode position," J. Neurosurg., 96(2) (Feb. 2002), pp. 269-279. cited by applicant .
Wakana, S., et al., "Fiber tract-based atlas of human white matter anatomy," Radiology, 230(1) (Jan. 2004), pp. 77-87. cited by applicant .
Alexander, DC., et al., "Spatial transformations of diffusion tensor magnetic resonance images," IEEE Transactions on Medical Imaging, 20 (11), (2001), pp. 1131-1139. cited by applicant .
Wu, Y. R., et al., "Does Stimulation of the GPi control dyskinesia by activating inhibitory axons?," Mov. Disord., vol. 16 (2001), pp. 208-216. cited by applicant .
Yelnik, J., et al., "Localization of stimulating electrodes in patients with Parkinson disease by using a three-dimensional atlas-magnetic resonance imaging coregistration method," J Neurosurg., 99(1) (Jul. 2003), pp. 89-99. cited by applicant .
Yianni, John, et al., "Globus pallidus internus deep brain stimulation for dystonic conditions: a prospective audit," Mov. Disord., vol. 18 (2003), pp. 436-442. cited by applicant .
Zonenshayn, M., et al., "Comparison of anatomic and neurophysiological methods for subthalamic nucleus targeting," Neurosurgery, 47(2) (Aug. 2000), pp. 282-294. cited by applicant .
Voghell et al., "Programmable Current Source Dedicated to Implantable Microstimulators" ICM '98 Proceedings of the Tenth International Conference, pp. 67-70. cited by applicant .
Butson, Christopher R. , et al., "Patient-specific analysis of the volume of tissue activated during deep brain stimulation", NeuroImage. vol. 34 (2007), 661-670. cited by applicant .
Adler, DE., et al., "The tentorial notch: anatomical variation, morphometric analysis, and classification in 100 human autopsy cases," J. Neurosurg., 96(6), (Jun. 2002), pp. 1103-1112. cited by applicant .
Jones et al., "An Advanced Demultiplexing System for Physiological Stimulation", IEEE Transactions on Biomedical Engineering, vol. 44 No. 12 Dec. 1997, pp. 1210-1220. cited by applicant .
Alo, K. M., et al., "New trends in neuromodulation for the management of neuropathic pain," Neurosurgery, 50(4), (Apr. 2002), pp. 690-703, discussion pp. 703-704. cited by applicant .
Ashby, P., et al., "Neurophysiological effects of stimulation through electrodes in the human subthalamic nucleus," Brain, 122 (PI 10), (Oct. 1999), pp. 1919-1931. cited by applicant .
Baker, K. B., et al., "Subthalamic nucleus deep brain stimulus evoked potentials: Physiological and therapeutic implications," Movement Disorders, 17(5), (Sep./Oct. 2002), pp. 969-983. cited by applicant .
Bammer, R, et al., "Diffusion tensor imaging using single-shot SENSE-EPI", Magn Reson Med., 48(1 ), (Jul. 2002), pp. 128-136. cited by applicant .
Basser, P J., et al., "MR diffusion tensor spectroscopy and imaging," Biophys J., 66(1 ), (Jan. 1994), pp. 259-267. cited by applicant .
Basser, P J., et al., "New currents in electrical stimulation of excitable tissues," Annu Rev Biomed Eng., 2, (2000), pp. 377-397. cited by applicant .
Benabid, AL., et al., "Chronic electrical stimulation of the ventralis intermedius nucleus of the thalamus as a treatment of movement disorders," J. Neurosurg., 84(2), (Feb. 1996), pp. 203-214. cited by applicant .
Benabid, AL., et al., "Combined (lhalamotoy and stimulation) stereotactic surgery of the VIM thalamic nucleus for bilateral Parkinson disease," Appl Neurophysiol, vol. 50, (1987), pp. 344-346. cited by applicant .
Benabid, A L., et al., "Long-term suppression of tremor by chronic stimulation of the ventral intermediate thalamic nucleus," Lancet, 337 (8738), (Feb. 16, 1991 ), pp. 403-406. cited by applicant .
Butson, C. R., et al., "Predicting the effects of deep brain stimulation with diffusion tensor based electric field models," Medical Image Computing and Computer-Assisted Intervention--Mic Cai 2006, Lecture Notes in Computer Science (LNCS), vol. 4191, pp. 429-437, LNCS, Springer, Berlin, DE. cited by applicant .
Christensen, Gary E., et al., "Volumetric transformation of brain anatomy," IEEE Transactions on Medical Imaging, 16(6), (Dec. 1997), pp. 864-877. cited by applicant .
Cooper, S , et al., "Differential effects of thalamic stimulation parameters on tremor and paresthesias in essential tremor," Movement Disorders, 17(Supp. 5), (2002), p. S193. cited by applicant .
Coubes, P , et al., "Treatment of DYT1-generalised dystonia by stimulation of the internal globus pallidus," Lancet, 355 (9222), (Jun. 24, 2000), pp. 2220-2221. cited by applicant .
Dasilva, A.F. M., et al., "A Primer Diffusion Tensor Imaging of Anatomical Substructures," Neurosurg. Focus; 15(1) (Jul. 2003), pp. 1-4. cited by applicant .
Dawant, B. M., et al., "Compuerized atlas-guided positioning of deep brain stimulators: a feasibility study," Biomedical Image registration, Second International Workshop, WBIR 2003, Revised Papers (Lecture notes in Comput. Sci. vol. (2717), Springer-Verlag Berlin, Germany(2003), pp. 142-150. cited by applicant .
Finnis, K. W., et al., "3-D functional atalas of subcortical structures for image guided stereotactic neurosurgery," Neuroimage, vol. 9, No. 6, Iss. 2 (1999), p. S206. cited by applicant .
Finnis, K. W., et al., "3D Functional Database of Subcorticol Structures for Surgical Guidance in Image Guided Stereotactic Neurosurgery," Medical Image Computing and Computer-Assisted Intervention--MICCAI'99, Second International Conference.Cambridge, UK, Sep. 19-22, 1999, Proceedings (1999), pp. 758-767. cited by applicant .
Finnis, K. W., et al., "A 3-Dimensional Database of Deep Brain Functional Anatomy, and Its Application to Image-Guided Neurosurgery," Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention.Lecture Notes in Computer Science; vol. 1935 (2000), pp. 1-8. cited by applicant .
Finnis, K. W., et al., "A functional database for guidance of surgical and therapeutic procedures in the deep brain," Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 3 (2000), pp. 1787-1789. cited by applicant .
Finnis, K. W., et al., "Application of a Population Based Electrophysiological Database to the Planning and Guidance of Deep Brain Stereotactic Neurosurgery," Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention--Part 11, Lecture Notes in Computer Science; vol. 2489 (2002), pp. 69-76. cited by applicant .
Finnis, K. W., et al., "Subcortical physiology deformed into a patient-specific brain atlas for image-guided stereotaxy," Proceedings of SPIE--vol. 4681 Medical Imaging 2002: Visualization, Image-Guided Procedures, and Display (May 2002), pp. 184-195. cited by applicant .
Finnis, Krik W., et al., "Three-Dimensional Database of Subcortical Electrophysiology for Image-Guided Stereotatic Functional Neurosurgery," IEEE Transactions on Medical Imaging, 22(1) (Jan. 2003), pp. 93-104. cited by applicant .
Gabriels, L , et al., "Deep brain stimulation for treatment-refractory obsessive-compulsive disorder: psychopathological and neuropsychological outcome in three cases," Acta Psychiatr Scand., 107(4) (2003), pp. 275-282. cited by applicant .
Gabriels, LA., et al., "Long-term electrical capsular stimulation in patients with obsessive-compulsive disorder," Neurosurgery, 52(6) (Jun. 2003), pp. 1263-1276. cited by applicant .
Goodall, E. V., et al., "Modeling study of activation and propagation delays during stimulation of peripheral nerve fibers with a tripolar cuff electrode," IEEE Transactions on Rehabilitation Engineering, [see also IEEE Trans. on Neural Systems and Rehabilitation], 3(3) (Sep. 1995), pp. 272-282. cited by applicant .
Goodall, E. V., et al., "Position-selective activation of peripheral nerve fibers with a cuff electrode," IEEE Transactions on Biomedical Engineering, 43(8) (Aug. 1996), pp. 851-856. cited by applicant .
Goodall, E. V., "Simulation of activation and propagation delay during tripolar neural stimulation," Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (1993), pp. 1203-1204. cited by applicant .
Grill, WM., "Modeling the effects of electric fields on nerve fibers: inftuence of tissue electrical properties," IEEE Transactions on Biomedical Engineering, 46(8) (1999), pp. 918-928. cited by applicant .
Grill, W. M., et al., "Neural and connective tissue response to long-term implantation of multiple contact nerve cuff electrodes," J Biomed Mater Res., 50(2) (May 2000), pp. 215-226. cited by applicant .
Grill, W. M., "Neural modeling in neuromuscular and rehabilitation research," Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 4 (2001 ), pp. 4065-4068. cited by applicant .
Grill, W. M., et al., "Non-invasive measurement of the input-output properties of peripheral nerve stimulating electrodes," Journal of Neuroscience Methods, 65(1) (Mar. 1996), pp. 43-50. cited by applicant .
Grill, W. M., et al., "Quantification of recruitment properties of multiple contact cuff electrodes," IEEE Transactions on Rehabilitation Engineering, [see also IEEE Trans. on Neural Systems and Rehabilitation], 4(2) (Jun. 1996), pp. 49-62. cited by applicant .
Grill, W. M., "Spatially selective activation of peripheral nerve for neuroprosthetic applications," Ph.D. Case Western Reserve University, (1995), pp. 245 pages. cited by applicant .
Grill, W. M., "Stability of the input-output properties of chronically implanted multiple contact nerve cuff stimulating electrodes," IEEE Transactions on Rehabilitation Engineering [see also IEEE Trans. on Neural Systems and Rehabilitation] (1998), pp. 364-373. cited by applicant .
Grill, W. M., "Stimulus waveforms for selective neural stimulation," IEEE Engineering in Medicine and Biology Magazine, 14(4) (Jul.-Aug. 1995), pp. 375-385. cited by applicant .
Grill, W. M., et al., "Temporal stability of nerve cuff electrode recruitment properties," IEEE 17th Annual Conference Engineering in Medicine and Biology Society, vol. 2 (1995), pp. 1089-1090. cited by applicant .
Gross, RE., et al., "Advances in neurostimulation for movement disorders," Neurol Res., 22(3) (Apr. 2000), pp. 247-258. cited by applicant .
Guridi et al., "The subthalamic nucleus, hemiballismus and Parkinson's disease: reappraisal of a neurological dogma," Brain, vol. 124, 2001, pp. 5-19. cited by applicant .
Haberler, C, et al., "No tissue damage by chronic deep brain stimulation in Parkinson's disease," Ann Neurol., 48(3) (Sep. 2000), pp. 372-376. cited by applicant .
Hamel, W, et al., "Deep brain stimulation of the subthalamic nucleus in Parkinson's disease: evaluation of active electrode contacts," J Neurol Neurosurg Psychiatry, 74(8) (Aug. 2003), pp. 1036-1046. cited by applicant .
Hanekom, "Modelling encapsulation tissue around cochlear implant electrodes," Med. Biol. Eng. Comput. vol. 43 (2005), pp. 47-55. cited by applicant .
Haueisen, J , et al., "The influence of brain tissue anisotropy on human EEG and MEG," Neuroimage, 15(1) (Jan. 2002), pp. 159-166. cited by applicant .
D'Haese et al. Medical Image Computing and Computer-Assisted Intervention--MICCAI 2005 Lecture Notes in Computer Science, 2005, vol. 3750, 2005, 427-434. cited by applicant .
Rohde et al. IEEE Transactions on Medical Imaging, vol. 22 No. 11, 2003 p. 1470-1479. cited by applicant .
Dawant et al., Biomedical Image Registration. Lecture Notes in Computer Science, 2003, vol. 2717, 2003, 142-150. cited by applicant .
Miocinovic et al., "Stereotactiv Neurosurgical Planning, Recording, and Visualization for Deep Brain Stimulation in Non-Human Primates", Journal of Neuroscience Methods, 162:32-41, Apr. 5, 2007, XP022021469. cited by applicant .
Gemmar et al., "Advanced Methods for Target Navigation Using Microelectrode Recordings in Stereotactic Neurosurgery for Deep Brain Stimulation", 21st IEEE International Symposium on Computer-Based Medical Systems, Jun. 17, 2008, pp. 99-104, XP031284774. cited by applicant .
Acar et al., "Safety Anterior Commissure-Posterior Commissure-Based Target Calculation of the Subthalamic Nucleus in Functional Stereotactic Procedures", Stereotactic Funct. Neurosura., 85:287-291, Aug. 2007. cited by applicant .
Andrade-Souza, "Comparison of Three Methods of Targeting the Subthalamic Nucleus for Chronic Stimulation in Parkinson's Disease", Neurosurgery, 56:360-368, Apr. 2005. cited by applicant .
Anheim et al., "Improvement in Parkinson Disease by Subthalamic Nucleus Stimulation Based on Electrode Placement", Arch Neural., 65:612-616, May 2008. cited by applicant .
Butson et al., "Tissue and Electrode Capacitance Reduce Neural Activation Volumes During Deep Brain Stimulation", Clinical Neurophysiology, 116:2490-2500, Oct. 2005. cited by applicant .
Butson et al., "Sources and Effects of Electrode Impedance During Deep Brain Stimulation", Clinical Neurophysiology, 117:44 7-454, Dec. 2005. cited by applicant .
D'Haese et al., "Computer-Aided Placement of Deep Brain Stimulators: From Planning to Intraoperative Guidance", IEEE Transaction on Medical Imaging, 24:1469-1478, Nov. 2005. cited by applicant .
Gross et al., "Electrophysiological Mapping for the Implantation of Deep Brain Stimulators for Parkinson's Disease and Tremor", Movement Disorders, 21 :S259-S283, Jun. 2006. cited by applicant .
Halpern et al., "Brain Shift During Deep Brain Stimulation Surgery for Parkinson's Disease", Stereotact Funct. Neurosurg., 86:37-43, published online Sep. 2007. cited by applicant .
Herzog et al., "Most Effective Stimulation Site in Subthalamic Deep Brain Stimulation for Parkinson's Disease", Movement Disorders, 19:1050-1099, published on line Mar. 2004. cited by applicant .
Jeon et al., A Feasibility Study of Optical Coherence Tomography for Guiding Deep Brain Probes, Journal of Neuroscience Methods, 154:96-101, Jun. 2006. cited by applicant .
Khan et al., "Assessment of Brain Shift Related to Deep Brain Stimulation Surgery", Sterreotact Funct. Neurosurg., 86:44-53, published online Sep. 2007. cited by applicant .
Koop et al., "Improvement in a Quantitative Measure of Bradykinesia After Microelectrode Recording in Patients with Parkinson's Disease During Deep Brain Stimulation Surgery", Movement Disorders, 21 :673-678, published on line Jan. 2006. cited by applicant .
Lemaire et al., "Brain Mapping in Stereotactic Surgery: A Brief Overview from the Probabilistic Targeting to the Patient-Based Anatomic Mapping", NeuroImage, 37:S109-S115, available online Jun. 2007. cited by applicant .
Machado et al., "Deep Brain Stimulation for Parkinson's Disease: Surgical Technique and Perioperative Management", Movement Disorders, 21 :S247-S258, Jun. 2006. cited by applicant .
Maks et al., "Deep Brain Stimulation Activation Volumes and Their Association with Neurophysiological Mapping and Therapeutic Outcomes", Downloaded from jnnp.bmj.com, pp. 1-21, published online Apr. 2008. cited by applicant .
Moran et al., "Real-Time Refinment of Subthalamic Nucleous Targeting Using Bayesian Decision-Making on the Root Mean Square Measure", Movement Disorders, 21: 1425-1431, published online Jun. 2006. cited by applicant .
Sakamoto et al., "Homogeneous Fluorescence Assays for RNA Diagnosis by Pyrene-Conjugated 2'-0-Methyloligoribonucleotides", Nucleosides, Nucleotides, and Nucleric Acids, 26:1659-1664, on line publication Oct. 2007. cited by applicant .
Winkler et al., The First Evaluation of Brain Shift During Functional Neurosurgery by Deformation Field Analysis, J. Neural. Neurosurg. Psychiatry, 76:1161-1163, Aug. 2005. cited by applicant .
Yelnik et al., "A Three-Dimensional, Histological and Deformable Atlas of the Human Basal J Ganglia. I. Atlas Construction Based on Immunohistochemical and MRI Data", NeuroImage, 34:618,-638,Jan. 2007. cited by applicant .
Ward, H. E., et al., "Update on deep brain stimulation for neuropsychiatric disorders," Neurobiol Dis 38 (3) (2010), pp. 346-353. cited by applicant .
Alberts et al. "Bilateral subthalamic stimulation impairs cognitive-motor performance in Parkinson's disease patients." Brain (2008), 131, 3348-3360, Abstract. cited by applicant .
Butson, Christopher R., et al., "Sources and effects of electrode impedance during deep brain stimulation", Clinical Neurophysiology. vol. 117.(2006),447-454. cited by applicant .
An, et al., "Prefronlal cortical projections to longitudinal columns in the midbrain periaqueductal gray in macaque monkeys," J Comp Neural 401 (4) (1998), pp. 455-479. cited by applicant .
Bulson, C. R., et al., "Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation," Clinical Neurophysiology, vol. 116 (2005), pp. 2490-2500. cited by applicant .
Carmichael, S. T., et al., "Connectional networks within the orbital and medial prefronlal cortex of macaque monkeys," J Comp Neural 371 (2) (1996), pp. 179-207. cited by applicant .
Croxson, et al., "Quantitative investigation of connections of the prefronlal cortex in the human and macaque using probabilistic diffusion tractography," J Neurosci 25 (39) (2005), pp. 8854-8866. cited by applicant .
Frankemolle, et al., "Reversing cognitive-motor impairments in Parkinson's disease patients using a computational modelling approach to deep brain stimulation programming," Brain 133 (2010), pp. 746-761. cited by applicant .
Freedman, et al., "Subcortical projections of area 25 (subgenual cortex) of the macaque monkey," J Comp Neurol 421 (2) (2000), pp. 172-188. cited by applicant .
Giacobbe, et al., "Treatment resistant depression as a failure of brain homeostatic mechanisms: implications for deep brain stimulation," Exp Neural 219 (1) (2009), pp. 44-52. cited by applicant .
Goodman, et al., "Deep brain stimulation for intractable obsessive compulsive disorder: pilot study using a blinded, staggered-onset design," Biol Psychiatry 67 (6) (2010), pp. 535-542. cited by applicant .
Greenberg, et al., "Deep brain stimulation of the ventral internal capsule/ventral striatum for obsessive-compulsive disorder: worldwide experience," Mol Psychiatry 15 (1) (2010), pp. 64-79. cited by applicant .
Greenberg. et al., "Three-year outcomes in deep brain stimulation for highly resistant obsessive-compulsive disorder," Neuropsychopharmacology 31 (11) (2006), pp. 2384-2393. cited by applicant .
Gutman, et al., "A tractography analysis of two deep brain stimulation white matter targets for depression," Biol Psychiatry 65 (4) (2009), pp. 276-282. cited by applicant .
Haber, et al., "Reward-related cortical inputs define a large striatal region in primates that interface with associative cortical connections, providing a substrate for incentive-based learning," J Neurosci 26 (32) (2006), pp. 8368-8376. cited by applicant .
Haber, et al., "Cognitive and limbic circuits that are affected by deep brain stimulation," Front Biosci 14 (2009), pp. 1823-1834. cited by applicant .
Hines, M. L., et al., "The NEURON simulation environment," Neural Comput., 9(6) (Aug. 15, 1997), pp. 1179-1209. cited by applicant .
Hua, et al., "Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification," Neuroimage 39 (1) (2008), pp. 336-347. cited by applicant .
Johansen-Berg, et al., "Anatomical connectivity of the subgenual cingulate region targeted with deep brain stimulation for treatment-resistant depression," Cereb Cortex 18 (6) (2008), pp. 1374-1383. cited by applicant .
Kopell, et al., "Deep brain stimulation for psychiatric disorders," J Clin Neurophysiol 21 (1) (2004), pp. 51-67. cited by applicant .
Lozano, et al., "Subcallosal cingulate gyrus deep brain stimulation for treatment-resistant depression," Biol Psychiatry 64 (6) (2008), pp. 461-467. cited by applicant .
Lujan, et al., "Tracking the mechanisms of deep brain stimulation for neuropsychiatric disorders," Front Biosci 13 (2008), pp. 5892-5904. cited by applicant .
Lujan, J.L. et al., "Automated 3-Dimensional Brain Atlas Fitting to Microelectrode Recordings from Deep Brain Stimulation Surgeries," Stereotact. Fune!. Neurosurg. 87(2009), pp. 229-240. cited by applicant .
Machado. et al., "Functional topography of the ventral striatum and anterior limb of the internal capsule determined by electrical stimulation of awake patients," Clin Neurophysiol 120 (11) (2009), pp. 1941-1948. cited by applicant .
Malone, et al., "Deep brain stimulation of the ventral capsule/ventral striatum for treatment-resistant depression," Biol Psychiatry 65 (4) (2009), pp. 267-275. cited by applicant .
Mayberg, H. S., et al., "Deep brain stimulation for treatment-resistant depression," Neuron, 45(5) (Mar. 3, 2005), pp. 651-660. cited by applicant .
Mayberg, H. S., et al., "Limbic-cortical dysregulation: a proposed model of depression" J Neuropsychiatry Clin Neurosci. 9 (3) (1997), pp. 471-481. cited by applicant .
McIntyre,C. C., et al., "Network perspectives on the mechanisms of deep brain stimulation," Neurobiol Dis 38 (3) (2010), pp. 329-337. cited by applicant .
Miocinovic, S., et al., "Experimental and theoretical characterization of the voltage distribution generated by deep brain stimulation," Exp Neurol 216 (i) (2009), pp. 166-176. cited by applicant .
Nuttin, et al., "Electrical stimulation in anterior limbs of internal capsules in patients with obsessive-compulsive disorder," Lancet 354 (9189) (1999), p. 1526. cited by applicant .
Saxena, et al., "Cerebral glucose metabolism in obsessive-compulsive hoarding," Am J Psychiatry. 161 (6) (2004), pp. 1038-1048. cited by applicant .
Viola, et al., "Importance-driven focus of attention," IEEE Trans Vis Comput Graph 12 (5) (2006), pp. 933-940. cited by applicant .
Wakana, S., et al., "Reproducibility of quantitative tractography methods applied to cerebral white matter," Neuroimage 36 (3) (2007), pp. 630-644. cited by applicant .
Mayr et al., "Basic Design and Construction of the Vienna FES Implants: Existing Solutions and Prospects for New Generations of Implants", Medical Engineering & Physics, 2001; 23:53-60. cited by applicant .
McIntyre, Cameron , et al., "Finite element analysis of the current-density and electric field generated by metal microelectrodes", Ann Biomed Eng . 29(3), (2001 ),227-235. cited by applicant .
Foster, K. R., et al., "Dielectric properties of tissues and biological materials: a critical review.", Grit Rev Biomed Ena. 17(1 ). {1989),25-104. cited by applicant .
Limousin, P., et al., "Electrical stimulation of the subthalamic nucleus in advanced Parkinson's disease", N Engl J Med .. 339(16), (Oct. 15, 1998), 1105-11. cited by applicant .
Kitagawa, M., et al., "Two-year follow-up of chronic stimulation of the posterior subthalamic white matter for tremor-dominant Parkinson's disease.", Neurosurgery. 56(2). (Feb. 2005),281-9. cited by applicant .
Johnson, M. D., et al., "Repeated voltage biasing improves unit recordings by reducing resistive tissue impedances", IEEE Transactions on Neural Systems and Rehabilitation Engineering, [see also IEEE Trans. on Rehabilitation Engineering (2005), 160-165. cited by applicant .
Holsheimer, J. , et al., "Chronaxie calculated from current-duration and voltage-duration data", J Neurosci Methods. 97(1). (Apr. 1, 2000),45-50. cited by applicant .
Hines, M. L., et al., "The NEURON simulation environment", Neural Comput. 9(6). (Aug. 15, 1997), 1179-209. cited by applicant .
Herzog, J., et al., "Most effective stimulation site in subthalamic deep brain stimulation for Parkinson's disease", Mov Disord. 19(9). (Sep. 2004),1050-4. cited by applicant .
Hershey, T., et al., "Cortical and subcortical blood flow effects of subthalamic nucleus stimulation in PD.", Neurology 61(6). (Sep. 23, 2003),816-21. cited by applicant .
Hemm, S. , et al., "Evolution of Brain Impedance in Dystonic Patients Treated by GPi Electrical Stimulation", Neuromodulation 7(2) (Apr. 2004),67-75. cited by applicant .
Hemm, S., et al., "Deep brain stimulation in movement disorders: stereotactic coregistration of two-dimensional electrical field modeling and magnetic resonance imaging.", J Neurosurg. 103(6): (Dec. 2005),949-55. cited by applicant .
Haueisen, J, et al., "The influence of brain tissue anisotropy on human EEG and MEG", Neuroimage 15(1) (Jan. 2002),159-166. cited by applicant .
Haslinger, B., et al., "Frequency-correlated decreases of motor cortex activity associated with subthalamic nucleus stimulation in Parkinson's disease.", Neuroimage 28(3). (Nov. 15, 2005),598-606. cited by applicant .
Hashimoto, T. , et al., "Stimulation of the subthalamic nucleus changes the firing pattern of pallidal neurons", J Neurosci. 23(5). (Mar. 1, 2003),1916-23. cited by applicant .
Hardman, C. D., et al., "Comparison of the basal ganglia in rats, marmosets, macaques, baboons, and humans: volume and neuronal number for the output, internal relay, and striatal modulating nuclei", J Comp Neurol., 445(3). (Apr. 8, 2002),238-55. cited by applicant .
McNaughtan et al., "Electrochemical Issues in Impedance Tomography", 1st World Congress on Industrial Process Tomography, Buxton, Greater Manchester, Apr. 14-17, 1999. cited by applicant .
Grill, WM., et al., "Electrical properties of implant encapsulation tissue", Ann Biomed Eng. vol. 22. (1994),23-33. cited by applicant.

Primary Examiner: Koharski; Christopher D
Assistant Examiner: Bays; Pamela M
Attorney, Agent or Firm: Lowe Graham Jones PLLC Black; Bruce E.

Parent Case Text



RELATED APPLICATION DATA

The present application claims the benefit under 35 U.S.C. .sctn.119 to U.S. provisional patent application Ser. No. 61/679,717, filed Aug. 4, 2012. The foregoing application is hereby incorporated by reference into the present application in its entirety.
Claims



What is claimed is:

1. A method of storing data in a neurostimulation system, the method comprising: generating a first image of a patient's brain and a second image of the patient's brain, the first image generated before a plurality of electrodes are implanted within the patient's brain and the second image generated after the plurality of electrodes are implanted within the patient's brain; generating patient-specific imaging-related data indicating a location of the plurality of electrodes within the patient's brain, the patient-specific imaging-related data generated based on both of the first and second images, wherein the patient-specific imaging-related data is at least one of imaging data of the patient's brain or a transformation data set to transform a generic 3D atlas into a patient-specific 3D atlas; and storing the patient-specific imaging-related data in a portable component of the neurostimulation system, wherein the portable component is selected from the group consisting of an implantable neurostimulator coupled to the plurality of electrodes implanted within the patient's brain and an external charger for transcutaneously charging the implantable neurostimulator, wherein the portable component is either a) implanted in the patient, b) configured and arranged to be worn by the patient, or c) configured and arranged to fit in a palm of a hand of the patient.

2. The method of claim 1, wherein generating the patient-specific imaging-related data comprises generating the imaging data of the patient's brain.

3. The method of claim 2, wherein the imaging data comprises at least one of magnetic resonance imaging data, diffusion tensor imaging data, and computed tomography scan data.

4. The method of claim 1, wherein generating the patient-specific imaging-related data comprises generating the transformation data set using a transformation procedure that transforms the generic 3D atlas into the patient-specific 3D atlas.

5. The method of claim 4, wherein the transformation data set comprises lead orientation information.

6. The method of claim 4, wherein the transformation data set comprises a 4.times.4 matrix.

7. The method of claim 4, wherein the transformation procedure comprises identifying at least three anatomical reference points in a given image of the patient's brain, and, based on locations of three corresponding reference points in the generic 3D atlas, transforming the generic 3D atlas into the patient-specific 3D atlas.

8. The method of claim 7, wherein the at least three anatomical reference points are an anterior commissure, a posterior commissure, and a mid-commissural point of the patient's brain.

9. The method of claim 1, wherein generating the patient-specific imaging-related data comprises performing a registration process between a series of magnetic resonance images and a computed tomography scan image, wherein the series of magnetic resonance images is obtained prior to implanting the plurality of electrodes, and the computer tomography scan image is obtained after implanting the plurality of electrodes.

10. The method of claim 1, wherein storing the patient-specific imaging-related data comprises storing the patient-specific imaging-related data in the implanted neurostimulator.

11. The method of claim 1, wherein storing the patient-specific imaging-related data comprises storing the patient-specific imaging-related data in the external charger.

12. A neurostimulator system comprising: a plurality of electrodes implanted within a patient's brain tissue; a portable component configured for storing patient-specific imaging-related data including a location of the plurality of electrodes within the patient's brain tissue, determined by imaging of the patient's brain tissue, as compared to an image of the patient's brain tissue prior to implantation of the plurality of electrodes, wherein the patient-specific imaging-related data is at least one of imaging data of the patient's brain tissue or a transformation data set to transform a generic 3D atlas into a patient-specific 3D atlas, the portable component selected from the group consisting of: an implantable neurostimulator and an external charger for transcutaneously charging the implantable neurostimulator, wherein the portable component is either a) implantable in the patient, b) configured and arranged to be worn by the patient, or c) configured and arranged to fit in a palm of a hand of the patient; and an external control device configured for obtaining the patient-specific imaging-related data from the portable component, generating a patient-specific anatomical atlas from the patient-specific image-related data, and programming, the portable component with at least one stimulation parameter based on the patient-specific anatomical atlas.

13. The system of claim 12, wherein the patient-specific imaging-related data is the imaging data of the patient's brain tissue.

14. The system of claim 13, wherein the imaging data comprises at least one of magnetic resonance imaging data, diffusion tensor imaging data, and computed tomography scan data.

15. The system of claim 12, wherein the patient-specific imaging-related data is the transformation data set, and wherein the external control device is further configured for generating the patient-specific 3D atlas from the transformation data set and a general anatomical atlas.

16. The system of claim 15, wherein the transformation data set comprises lead orientation information.

17. The system of claim 15, wherein the transformation data set comprises a 4.times.4 matrix.

18. The system of claim 15, wherein the external control device is configured for simulating a volume of tissue activation for each of one or more candidate stimulation parameters, wherein the simulation is based at least in part on the patient-specific imaging-related data, and selecting at least one of the one or more candidate stimulation parameters, and programming the implantable neurostimulator with the at least one selected stimulation parameter.

19. A method for programming an implantable neurostimulator coupled to a plurality of electrodes that are implanted within a patient's brain, comprising: imaging the patient's brain to determine an implanted location of the plurality of electrodes within the patient's brain; receiving patient-specific imaging-related data from a portable component selected from the group consisting of: the implantable neurostimulator and an external charger for transcutaneously charging the implantable neurostimulator, wherein the portable component is either a) implantable in the patient, b) configured and arranged to be worn by the patient, or c) configured and arranged to fit in a palm of a hand of the patient, wherein the patient-specific imaging-related data is at least one of imaging data of the patient's brain or a transformation data set to transform a generic 3D atlas into a patient-specific 3D atlas; generating a patient-specific anatomical atlas from the patient-specific image-related data including the location of the plurality of electrodes within the patient's brain, determined by the imaging of the patient's brain, as compared to an image of the patient's brain prior to implantation of the plurality of electrodes; and programming the portable component with at least one stimulation parameter based on the patient-specific anatomical atlas.

20. The method of claim 19, wherein the patient-specific imaging-related data comprises the imaging data of the patient's brain.

21. The method of claim 20, wherein the imaging data comprises at least one of magnetic resonance imaging data, diffusion tensor imaging data, and computed tomography scan data.

22. The method of claim 19, wherein the patient-specific imaging-related data is the transformation data set, and wherein the method further comprises using the transformation data set to transform the generic 3D atlas into the patient-specific 3D atlas.

23. The method of claim 22, wherein the transformation data set comprises lead orientation information.

24. The method of claim 22, wherein the transformation data set comprises a 4.times.4 matrix.

25. The method of claim 19, further comprising: simulating a volume of tissue activation for each of one or more candidate stimulation parameters, wherein the simulation is based at least in part on the patient-specific imaging-related data; selecting at least one of the one or more candidate stimulation parameters; and programming the implantable neurostimulator with the at least one selected stimulation parameter.
Description



FIELD OF THE INVENTION

The present invention relates to tissue stimulation systems, and more particularly, to implantable stimulators and methods for programming the implantable stimulators.

BACKGROUND OF THE INVENTION

Implantable neurostimulation systems have proven therapeutic in a wide variety of diseases and disorders. Pacemakers and Implantable Cardiac Defibrillators (ICDs) have proven highly effective in the treatment of a number of cardiac conditions (e.g., arrhythmias). Spinal Cord Stimulation (SCS) systems have long been accepted as a therapeutic modality for the treatment of chronic pain syndromes, and the application of tissue stimulation has begun to expand to additional applications, such as angina pectoris and incontinence. Further, in recent investigations, Peripheral Nerve Stimulation (PNS) systems have demonstrated efficacy in the treatment of chronic pain syndromes and incontinence, and a number of additional applications are currently under investigation.

More pertinent to the present inventions described herein, Deep Brain Stimulation (DBS) has been applied therapeutically for well over a decade for the treatment of neurological disorders, including Parkinson's Disease, essential tremor, dystonia, and epilepsy, to name but a few. Further details discussing the treatment of diseases using DBS are disclosed in U.S. Pat. Nos. 6,845,267, and 6,950,707, which are expressly incorporated herein by reference.

Each of these implantable neurostimulation systems typically includes one or more electrode carrying stimulation leads, which are implanted at the desired stimulation site, and a neurostimulator implanted remotely from the stimulation site, but coupled either directly to the neurostimulation lead(s) or indirectly to the neurostimulation lead(s) via a lead extension. The neurostimulation system may further comprise a handheld external control device to remotely instruct the neurostimulator to generate electrical stimulation pulses in accordance with selected stimulation parameters. Typically, the stimulation parameters programmed into the neurostimulator can be adjusted by manipulating controls on the external control device to modify the electrical stimulation provided by the neurostimulator system to the patient.

Thus, in accordance with the stimulation parameters programmed by the external control device, electrical pulses can be delivered from the neurostimulator to the stimulation electrode(s) to stimulate or activate a volume of tissue in accordance with a set of stimulation parameters and provide the desired efficacious therapy to the patient. The best stimulation parameter set will typically be one that delivers stimulation energy to the volume of tissue that must be stimulated in order to provide the therapeutic benefit (e.g., treatment of movement disorders), while minimizing the volume of non-target tissue that is stimulated. A typical stimulation parameter set may include the electrodes that are acting as anodes or cathodes, as well as the amplitude, duration, and rate of the stimulation pulses.

Programming a neurostimulator (e.g., a DBS stimulator for treating movement disorders) can be a laborious and time intensive process that can take many programming sessions over several months to complete. In the context of DBS, neurostimulation leads with a complex arrangement of electrodes that not only are distributed axially along the leads, but are also distributed circumferentially around the neurostimulation leads as segmented electrodes, can be used. The large number of electrodes available, combined with the ability to generate a variety of complex stimulation pulses, presents a huge selection of stimulation parameter sets to the clinician or patient.

To facilitate such selection, the clinician generally programs the external control device, and if applicable the neurostimulator, through a computerized programming system. This programming system can be a self-contained hardware/software system, or can be defined predominantly by software running on a standard personal computer (PC). The PC or custom hardware may actively control the characteristics of the electrical stimulation generated by the neurostimulator to allow the optimum stimulation parameters to be determined based on patient feedback and to subsequently program the external control device with the optimum stimulation parameters.

To facilitate determination of the location of the electrodes relative to the target tissue region or regions, and even the non-target tissue region or regions, the computerized programming system may optionally be capable of storing one or more anatomical regions of interest, which may be registered with the neurostimulation leads when implanted with the patient. The anatomical region of interest may be obtained from a generally available atlas, and in the case of DBS, a brain atlas. Although the use of a generalized brain atlas may be quite helpful when optimizing the stimulation parameters that are ultimately programmed into the neurostimulation system, these types of atlases are not patient specific, and thus, cannot account for patient specific physiology.

After the DBS system has been implanted and fitted, post-implant programming sessions are typically required if the treatment provided by the implanted DBS system is no longer effective or otherwise is not therapeutically or operationally optimum due to, e.g., disease progression, motor re-learning, or other changes. As physicians and clinicians become more comfortable with implanting neurostimulation systems and time in the operating room decreases, post-implant programming sessions are becoming a larger portion of the process.

Regardless of the skill of the physician or clinician, neurostimulation programming sessions can be especially lengthy when programming complicated neurostimulation systems, such as DBS systems, where patients usually cannot feel the effects of stimulation, and the effects of the stimulation may be difficult to observe, are typically subjective, or otherwise may take a long time to become apparent. Clinical estimates suggest that 18-36 hours per patient are necessary to program and assess DBS patients with current techniques (see Hunka K., et al., Nursing Time to Program and Assess Deep Brain Stimulators in Movement Disorder Patients, J. Neursci Nurs. 37: 204-10), which is an extremely large time commitment for both the physician/clinician and the patient.

Recent advances in DBS programming systems include the ability to predict and visualize the stimulation field based on the position of the lead in the anatomy and the electrode configuration. The anatomy is scaled to map to the patient's brain via a process called "registration." Registration involves using the pre-op MR images and post-op CT images of a patient, and generating a "transformation" data set that enables the scaling of a generic 3D brain atlas to represent the specific patient's brain. In addition to the transformation data set, additional information such as the lead data (model, electrodes size/shape/position, connections to the stimulator, orientation of the lead in the brain, etc.) are key to predicting the stimulation field.

It can be appreciated from this that the availability of patient-specific data (e.g., a patient-specific 3D atlas, a lead orientation relative to the patient's tissue, imaging data for the patient, and clinical effects for the patient) has a significant impact on the complexity of, and the amount of time required for, programming a neurostimulator. This patient-specific data may be readily available during implantation of the neurostimulator and is stored within the computerized programming system used during neurostimulator implantation in the operating room. However, once implanted, subsequent programming of the neurostimulator may be impacted by the availability of this patient-specific data. Because the patient-specific data is only stored in the computerized programming system used in the operating room, this same computerized programming system must be used during the navigation session or follow-up reprogramming session, or the patient-specific data must be transferred from the operating room computerized programming system to the new computerized programming system. However, in a clinical setting, it is quite common that the same computerized programming system is not available to program the neurostimulator, and there is a high likelihood that the neurostimulator is programmed or reprogrammed using a different computerized programming system (either in the same hospital/clinic or in a different hospital/clinic).

Significantly contributing to the lengthy process of programming a neurostimulation system is the fact that patient-specific data may not be available during a programming session. Thus, there remains a need for an improved neurostimulator system that allows an external control device to program a neurostimulator implanted within a patient without having prior knowledge of patient-specific data.

SUMMARY OF THE INVENTION

In accordance with a first aspect of the present inventions, a method of storing data in a neurostimulation system is provided. The method includes generating patient-specific imaging-related data, and storing the patient-specific imaging-related data in the at least one portable component. The portable component may be an implantable neurostimulator coupled to a plurality of electrodes implanted within the tissue of a patient, a patient's remote controller used for telemetrically controlling the implantable neurostimulator, and/or an external charger for transcutaneously charging the implantable neurostimulator.

In one embodiment, generating the patient-specific imaging-related data may include generating imaging data of the tissue of the patient (e.g., magnetic resonance imaging data, diffusion tensor imaging data, and/or computed tomography scan data).

In another embodiment, generating the patient-specific imaging-related data may include generating a transformation data set using a transformation procedure that transforms a generic 3D atlas into a patient-specific 3D atlas. The transformation data set may include lead orientation information. The transformation procedure may include identifying at least three anatomical reference points in an image of the patient's brain, and, based on locations of three corresponding reference points in the generic 3D atlas, transforming the generic 3D atlas into a patient-specific 3D atlas. The at least three anatomical reference points may include an anterior commissure, a posterior commissure, and a mid-commissural point of the patient's brain. The transformation data set may be a 4.times.4 matrix.

In one embodiment, generating the patient-specific imaging-related data may include performing a registration process between a series of magnetic resonance images and a computed tomography scan image, wherein the series of magnetic resonance images is obtained prior to implanting the plurality of electrodes, and the computer tomography scan image is obtained after implanting the plurality of electrodes.

In accordance with another aspect of the present inventions, a neurostimulator system is provided. The neurostimulator system includes a portable component configured for storing patient-specific imaging-related data, the portable component selected from the group consisting of: an implantable neurostimulator, a patient's remote controller used for telemetrically controlling the implantable neurostimulator, and an external charger for transcutaneously charging the implantable neurostimulator. The neurostimulator system further includes an external control device configured for obtaining the patient-specific imaging-related data from the portable component, generating a patient-specific anatomical atlas from the patient-specific image-related data, and programming the portable component with at least one stimulation parameter based on the patient-specific anatomical atlas.

In one embodiment, the patient-specific imaging-related data may be imaging data for the tissue of the patient (e.g., magnetic resonance imaging data, diffusion tensor imaging data, and/or computed tomography scan data).

In another embodiment, the patient-specific imaging-related data may be a transformation data set, and the external control device may be further configured for generating the patient-specific anatomical atlas from the transformation data set and a general anatomical atlas. The transformation data set may include lead orientation information. The transformation data set may be a 4.times.4 matrix.

The external control device may optionally be configured for simulating a volume of tissue activation for each of one or more candidate stimulation parameters, wherein the simulation is based at least in part on the patient-specific imaging-related data, and selecting at least one of the candidate stimulation parameters, and programming the implantable neurostimulator with the selected stimulation parameters.

In accordance with yet another aspect of the present inventions, a method for programming an implantable neurostimulator coupled to a plurality of electrodes that are implanted within the tissue of a patient is provided. The method includes receiving patient-specific imaging-related data from a portable component selected from the group consisting of: the implantable neurostimulator, a patient's remote controller used for telemetrically controlling the implantable neurostimulator, and an external charger for transcutaneously charging the implantable neurostimulator.

The patient-specific imaging-related data may be a transformation data set, and the method may further include using the transformation data set to transform a general atlas into a patient-specific atlas. The transformation data set may include lead orientation information. The transformation data set may be a 4.times.4 matrix.

The patient-specific imaging-related data may include imaging data for the tissue of the patient. For example, the imaging data may include at least one of magnetic resonance imaging data, diffusion tensor imaging data, and computed tomography scan data.

The method may optionally comprise simulating a volume of tissue activation for each of one or more candidate stimulation parameters, wherein the simulation is based at least in part on the patient-specific imaging-related data. Still further, the method includes selecting at least one of the candidate stimulation parameters, and programming the implantable neurostimulator with the selected stimulation parameters.

Other and further aspects and features of the invention will be evident from reading the following detailed description of the preferred embodiments, which are intended to illustrate, not limit, the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate the design and utility of preferred embodiments of the present inventions, in which similar elements are referred to by common reference numerals. In order to better appreciate how the above-recited and other advantages and objects of the present inventions are obtained, a more particular description of the present inventions briefly described above will be rendered by reference to specific embodiments thereof, which are illustrated in the accompanying drawings. Understanding that these drawings depict only typical embodiments of the inventions and are not therefore to be considered limiting of its scope, the inventions will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIG. 1 is a plan view of a Deep Brain Stimulation (DBS) system constructed in accordance with one embodiment of the present inventions;

FIG. 2 is a profile view of an implantable pulse generator (IPG) and a first embodiment of neurostimulation leads used in the DBS system of FIG. 1;

FIG. 3 is a profile view of an implantable pulse generator (IPG) and a second embodiment of neurostimulation leads used in the DBS system of FIG. 1;

FIG. 4 is a cross-sectional view of one of the neurostimulation leads of FIG. 3, taken along the line 4-4;

FIG. 5 is a cross-sectional view of a patient's head showing the implantation of stimulation leads and an IPG of the DBS system of FIG. 1;

FIG. 6 is front view of a remote control (RC) used in the DBS system of FIG. 1;

FIG. 7 is a block diagram of the internal components of the RC of FIG. 6;

FIG. 8 is a block diagram of the internal components of a clinician's programmer (CP) used in the DBS system of FIG. 1;

FIG. 9 is a flow chart of a method for storing data in a neurostimulation system; and

FIG. 10 is a flow chart of a method for programming an implantable neurostimulator.

DETAILED DESCRIPTION OF THE EMBODIMENTS

At the outset, it is noted that the present inventions may be used with an implantable pulse generator (IPG), radio frequency (RF) transmitter, or similar neurostimulator, that may be used as a component of numerous different types of stimulation systems. The description that follows relates to a deep brain stimulation (DBS) system. However, it is to be understood that the while the invention lends itself well to applications in DBS, the invention, in its broadest aspects, may not be so limited. Rather, the invention may be used with any type of implantable electrical circuitry used to stimulate tissue. For example, the present invention may be used as part of a pacemaker, a defibrillator, a cochlear stimulator, a retinal stimulator, a stimulator configured to produce coordinated limb movement, a cortical stimulator, a spinal cord stimulator, peripheral nerve stimulator, microstimulator, or in any other neural stimulator configured to treat urinary incontinence, sleep apnea, shoulder sublaxation, headache, etc.

Turning first to FIG. 1, an exemplary DBS neurostimulation system 10 generally includes at least one implantable stimulation lead 12 (in this case, two), a neurostimulator in the form of an implantable pulse generator (IPG) 14, an external remote controller RC 16, a clinician's programmer (CP) 18, an External Trial Stimulator (ETS) 20, and an external charger 22. The IPG 14, RC 16, and charger 22 may be considered to be portable components (i.e., either capable of being implanted within the patient, worn by the patient, or carried by the patient in palm of his or her hand).

The IPG 14 is physically connected via one or more percutaneous lead extensions 24 to the neurostimulation leads 12, which carry a plurality of electrodes 26 arranged in an array. In the illustrated embodiment, the neurostimulation leads 12 are percutaneous leads, and to this end, the electrodes 26 may be arranged in-line along the neurostimulation leads 12. In alternative embodiments, the electrodes 26 may be arranged in a two-dimensional pattern on a single paddle lead if, e.g., cortical brain stimulation is desired. As will be described in further detail below, the IPG 14 includes pulse generation circuitry that delivers electrical stimulation energy in the form of a pulsed electrical waveform (i.e., a temporal series of electrical pulses) to the electrode array 26 in accordance with a set of stimulation parameters.

The ETS 20 may also be physically connected via the percutaneous lead extensions 28 and external cable 30 to the neurostimulation leads 12. The ETS 20, which has similar pulse generation circuitry as the IPG 14, also delivers electrical stimulation energy in the form of a pulse electrical waveform to the electrode array 26 accordance with a set of stimulation parameters. The major difference between the ETS 20 and the IPG 14 is that the ETS 20 is a non-implantable device that is used on a trial basis after the neurostimulation leads 12 have been implanted and prior to implantation of the IPG 14, to test the responsiveness of the stimulation that is to be provided. Thus, any functions described herein with respect to the IPG 14 can likewise be performed with respect to the ETS 20.

The RC 16 may be used to telemetrically control the ETS 20 via a bi-directional RF communications link 32. Once the IPG 14 and stimulation leads 12 are implanted, the RC 16 may be used to telemetrically control the IPG 14 via a bi-directional RF communications link 34. Such control allows the IPG 14 to be turned on or off and to be programmed with different stimulation parameter sets. The IPG 14 may also be operated to modify the programmed stimulation parameters to actively control the characteristics of the electrical stimulation energy output by the IPG 14. As will be described in further detail below, the CP 18 provides clinician detailed stimulation parameters for programming the IPG 14 and ETS 20 in the operating room and in follow-up sessions.

The CP 18 may perform this function by indirectly communicating with the IPG 14 or ETS 20, through the RC 16, via an IR communications link 36. Alternatively, the CP 18 may directly communicate with the IPG 14 or ETS 20 via an RF communications link (not shown). The clinician detailed stimulation parameters provided by the CP 18 are also used to program the RC 16, so that the stimulation parameters can be subsequently modified by operation of the RC 16 in a stand-alone mode (i.e., without the assistance of the CP 18).

The external charger 22 is a portable device used to transcutaneously charge the IPG 14 via an inductive link 38. For purposes of brevity, the details of the external charger 22 will not be described herein. Details of exemplary embodiments of external chargers are disclosed in U.S. Pat. No. 6,895,280, which has been previously incorporated herein by reference. Once the IPG 14 has been programmed, and its power source has been charged by the external charger 22 or otherwise replenished, the IPG 14 may function as programmed without the RC 16 or CP 18 being present.

Referring to FIG. 2, the IPG 14 comprises an outer case 40 for housing the electronic and other components (described in further detail below), and a connector 42 to which the proximal end of the neurostimulation lead 12 mates in a manner that electrically couples the electrodes 26 to the internal electronics (described in further detail below) within the outer case 40. The outer case 40 is composed of an electrically conductive, biocompatible material, such as titanium, and forms a hermetically sealed compartment wherein the internal electronics are protected from the body tissue and fluids. In some cases, the outer case 40 may serve as an electrode.

Each of the neurostimulation leads 12 comprises an elongated cylindrical lead body 44, and the electrodes 26 take the form of ring electrodes mounted around the lead body 44. One of the neurostimulation leads 12 has eight electrodes 26 (labeled E1-E8), and the other neurostimulation lead 12 has eight electrodes 26 (labeled E9-E16). The actual number and shape of leads and electrodes will, of course, vary according to the intended application.

Further details describing the construction and method of manufacturing percutaneous stimulation leads are disclosed in U.S. patent application Ser. No. 11/689,918, entitled "Lead Assembly and Method of Making Same," and U.S. patent application Ser. No. 11/565,547, entitled "Cylindrical Multi-Contact Electrode Lead for Neural Stimulation and Method of Making Same," the disclosures of which are expressly incorporated herein by reference.

In an alternative embodiment illustrated in FIG. 3, the electrodes 26 take the form of segmented electrodes that are circumferentially and axially disposed about the lead body 44. By way of non-limiting example, and with further reference to FIG. 4, one neurostimulation lead 12 may carry sixteen electrodes, arranged as four rings of electrodes (the first ring consisting of electrodes E1-E4; the second ring consisting of electrodes E5-E8; the third ring consisting of electrodes E9-E12; and the fourth ring consisting of E13-E16) or four axial columns of electrodes (the first column consisting of electrodes E1, E5, E9, and E13; the second column consisting of electrodes E2, E6, E10, and E14; the third column consisting of electrodes E3, E7, E11, and E15; and the fourth column consisting of electrodes E4, E8, E12, and E16).

Further details describing the construction and method of manufacturing segmented stimulation leads are disclosed in U.S. patent application Ser. No. 13/212,063, entitled "User Interface for Segmented Neurostimulation Leads," which is expressly incorporated herein by reference.

The IPG 14 further comprises a microcontroller 46 that carries out a program function in accordance with a suitable program stored in memory (not shown). Thus, the microcontroller 46 generates the necessary control and status signals, which allow the microcontroller 46 to control the operation of the IPG 14 in accordance with a selected operating program and stimulation parameters stored within memory 48. Such stimulation parameters may comprise electrode combinations, which define the electrodes that are activated as anodes (positive), cathodes (negative), and turned off (zero), percentage of stimulation energy assigned to each electrode (fractionalized electrode configurations), and electrical pulse parameters, which define the pulse amplitude (measured in milliamps or volts depending on whether the IPG 14 supplies constant current or constant voltage to the electrode array 26), pulse duration (measured in microseconds), pulse rate (measured in pulses per second), and burst rate (measured as the stimulation on duration X and stimulation off duration Y). The IPG 14 may be capable of delivering the stimulation energy to the array 22 over multiple channels or over only a single channel. As will be described in further detail below, the memory 48 may also store patient-specific imaging-related data, as well as lead orientation data and clinical effects data, as discussed in U.S. patent application Ser. No. 13/292,989, entitled "System and Method for Storing Application Specific and Lead Configuration Information in Neurostimulation Device," and U.S. patent application Ser. No. 13/481,524, entitled "Collection of Clinical Data for Graphical Representation and Analysis," which are expressly incorporated herein by reference.

The IPG 14 further comprises telemetry circuitry 50 (including antenna) configured for receiving programming data (e.g., the operating program and/or stimulation parameters) from the RC 16 in an appropriate modulated carrier signal, and demodulating the carrier signal to recover the programming data, which programming data is then stored within the memory. The telemetry circuitry 50 also provides status data to the RC 16.

The IPG 14 further comprises a rechargeable power source 52 for providing the operating power to the IPG 14. The rechargeable power source 52 may, e.g., comprise a lithium-ion or lithium-ion polymer battery. The rechargeable power source 52 is recharged using rectified AC power (or DC power converted from AC power through other means, e.g., efficient AC-to-DC converter circuits, also known as "inverter circuits") received by an AC receiving coil (not shown). To recharge the power source 52, the external charger 22 (shown in FIG. 1), which generates the AC magnetic field, is placed against, or otherwise adjacent, to the patient's skin over the implanted IPG 14. The AC magnetic field emitted by the external charger 22 induces AC currents in the AC receiving coil. Charging circuitry (not shown) rectifies the AC current to produce DC current, which is used to charge the power source 52.

It should be noted that rather than having a fully contained IPG, the system 10 may alternatively utilize an implantable receiver-modulator (not shown) connected to the catheter(s) 12. In this case, the power source, e.g., a battery, for powering the implanted receiver, as well as control circuitry to command the receiver-stimulator, will be contained in an external controller inductively coupled to the receiver-stimulator via an electromagnetic link. Data/power signals are transcutaneously coupled from a cable-connected transmission coil placed over the implanted receiver-modulator. The implanted receiver-modulator receives the signal and delivers the therapy in accordance with the control signals.

As shown in FIG. 5, two percutaneous neurostimulation leads 12 are introduced through a burr hole 62 (or alternatively, two respective burr holes) formed in the cranium 64 of a patient 60, and introduced into the parenchyma of the brain 66 of the patient 60 in a conventional manner, such that the electrodes 26 are adjacent a target tissue region, the stimulation of which will treat the dysfunction (e.g., the ventrolateral thalamus, internal segment of globus pallidus, substantia nigra pars reticulate, subthalamic nucleus, or external segment of globus pallidus). Thus, stimulation energy can be conveyed from the electrodes 26 to the target tissue region to change the status of the dysfunction. Due to the lack of space near the location where the stimulation leads 12 exit the burr hole 62, the IPG 14 is generally implanted in a surgically-made pocket either in the chest, or in the abdomen. The IPG 14 may, of course, also be implanted in other locations of the patient's body. The lead extension(s) 24 facilitates locating the IPG 14 away from the exit point of the stimulation leads 12.

Referring now to FIG. 6, one exemplary embodiment of an RC 16 will now be described. As previously discussed, the RC 16 is capable of communicating with the IPG 14, CP 18, or ETS 20. The RC 16 comprises a casing 100, which houses internal componentry (including a printed circuit board (PCB)), and a lighted display screen 102 and button pad 104 carried by the exterior of the casing 100. In the illustrated embodiment, the display screen 102 is a lighted flat panel display screen, and the button pad 104 comprises a membrane switch with metal domes positioned over a flex circuit, and a keypad connector connected directly to a PCB. In an optional embodiment, the display screen 102 has touchscreen capabilities. The button pad 104 includes a multitude of buttons 106, 108, 110, and 112, which allow the IPG 14 to be turned ON and OFF, provide for the adjustment or setting of stimulation parameters within the IPG 14, and provide for selection between screens.

In the illustrated embodiment, the button 106 serves as an ON/OFF button that can be actuated to turn the IPG 14 ON and OFF. The button 108 serves as a select button that allows the RC 16 to switch between screen displays and/or parameters. The buttons 110 and 112 serve as up/down buttons that can actuated to increment or decrement any of stimulation parameters of the pulse generated by the IPG 14, including pulse amplitude, pulse width, and pulse rate. For example, the selection button 108 can be actuated to place the RC 16 in an "Pulse Amplitude Adjustment Mode," during which the pulse amplitude can be adjusted via the up/down buttons 110, 112, a "Pulse Width Adjustment Mode," during which the pulse width can be adjusted via the up/down buttons 110, 112, and a "Pulse Rate Adjustment Mode," during which the pulse rate can be adjusted via the up/down buttons 110, 112. Alternatively, dedicated up/down buttons can be provided for each stimulation parameter. Rather than using up/down buttons, any other type of actuator, such as a dial, slider bar, or keypad, can be used to increment or decrement the stimulation parameters. Further details of the functionality and internal componentry of the RC 16 are disclosed in U.S. Pat. No. 6,895,280, which has previously been incorporated herein by reference.

Referring to FIG. 7, the internal components of an exemplary RC 16 will now be described. The RC 16 generally includes a controller/processor 114 (e.g., a microcontroller), memory 116 that stores an operating program for execution by the controller/processor 114, as well as stimulation parameter sets. The memory 116 may also store patient-specific imaging-related data, as well as lead orientation data and clinical effects data, as discussed in U.S. patent application Ser. No. 13/292,989, entitled "System and Method for Storing Application Specific and Lead Configuration Information in Neurostimulation Device," and U.S. patent application Ser. No. 13/481,524, entitled "Collection of Clinical Data for Graphical Representation and Analysis," which have been expressly incorporated herein by reference.

The RC 16 further comprises telemetry circuitry 118 for outputting stimulation parameters to the IPG 14 and receiving status information from the IPG 14, and input/output circuitry 120 for receiving stimulation control signals from the button pad 104 and transmitting status information to the display screen 102 (shown in FIG. 6). As well as controlling other functions of the RC 16, which will not be described herein for purposes of brevity, the controller/processor 114 generates new stimulation parameter sets in response to the user operation of the button pad 104. These new stimulation parameter sets would then be transmitted to the IPG 14 via the telemetry circuitry 118. Further details of the functionality and internal componentry of the RC 16 are disclosed in U.S. Pat. No. 6,895,280, which has previously been incorporated herein by reference. Notably, while the controller/processor 114 is shown in FIG. 7 as a single device, the processing functions and controlling functions can be performed by a separate controller and processor.

As briefly discussed above, the CP 18 greatly simplifies the programming of multiple electrode combinations, allowing the physician or clinician to readily determine the desired stimulation parameters to be programmed into the IPG 14, as well as the RC 16. Thus, modification of the stimulation parameters in the programmable memory of the IPG 14 after implantation is performed by a clinician using the CP 18, which can directly communicate with the IPG 14 or indirectly communicate with the IPG 14 via the RC 16. That is, the CP 18 can be used by the physician or clinician to modify operating parameters of the electrode array 26 in the brain.

The overall appearance of the CP 18 is that of a laptop personal computer (PC), and in fact, may be implanted using a PC that has been appropriately configured to include a directional-programming device and programmed to perform the functions described herein. Alternatively, the CP 18 may take the form of a mini-computer, personal digital assistant (PDA), smartphone, etc., or even a remote control (RC) with expanded functionality. Thus, the programming methodologies can be performed by executing software instructions contained within the CP 18. Alternatively, such programming methodologies can be performed using firmware or hardware. In any event, the CP 18 may actively control the characteristics of the electrical stimulation generated by the IPG 14 to allow the optimum stimulation parameters to be determined based on patient response and feedback and for subsequently programming the IPG 14 with the optimum stimulation parameters.

Referring to FIG. 8, to allow the user to perform these functions, the CP 18 includes a standard user input device 122 (e.g., a keyboard, mouse, joystick, etc.) to allow a clinician to input information and control the process and a display monitor 126 housed in a case. In the illustrated embodiment, the monitor 126 is a conventional screen. Alternatively, instead of being conventional, the monitor 126 may be a digitizer screen, such as touchscreen (not shown), and may be used in conjunction with an active or passive digitizer stylus/finger touch. The CP 18 generally includes a controller/processor 130 (e.g., a central processor unit (CPU)) and memory 132 that stores a stimulation programming package 134, which can be executed by the controller/processor 130 to allow the user to program the IPG 14, and RC 16. The CP 18 further includes telemetry circuitry 136 for downloading stimulation parameters to the IPG 14 and RC 16 and for uploading stimulation parameters (as well as patient-specific imaging related data) already stored in the IPG 14 and RC 16. Notably, while the controller/processor 130 is shown in FIG. 8 as a single device, the processing functions and controlling functions can be performed by a separate controller and processor. Thus, it can be appreciated that the controlling functions described below as being performed by the CP 18 can be performed by a controller, and the processing functions described below as being performed by the CP 18 can be performed by a processor.

Execution of the programming package 134 by the controller/processor 130 provides a multitude of display screens (not shown) that can be navigated through via use of the user input device 122. These display screens allow the clinician to, among other functions, to select or enter patient profile information (e.g., name, birth date, patient identification, physician, diagnosis, and address), enter procedure information (e.g., programming/follow-up, implant trial system, implant IPG, implant IPG and lead(s), replace IPG, replace IPG and leads, replace or revise leads, explant, etc.), generate a therapeutic map (e.g., body regions targeted for therapy, body regions for minimization of side-effects, along with metrics (e.g., Unified Parkinson's Disease Rating Scale (UPDRS)) of success for said targets) of the patient, define the configuration and orientation of the leads, initiate and control the electrical stimulation energy output by the leads 12, and select and program the IPG 14 with stimulation parameters in both a surgical setting and a clinical setting. Further details discussing the above-described CP functions are disclosed in U.S. patent application Ser. No. 12/501,282, entitled "System and Method for Converting Tissue Stimulation Programs in a Format Usable by an Electrical Current Steering Navigator," and U.S. patent application Ser. No. 12/614,942, entitled "System and Method for Determining Appropriate Steering Tables for Distributing Stimulation Energy Among Multiple Neurostimulation Electrodes," which are expressly incorporated herein by reference.

The user interface includes a series of programming screens with various control elements that can be actuated to perform functions corresponding to the control elements. In the illustrated embodiment, control elements are implemented as a graphical icon that can be clicked with a mouse in the case of a conventional display device. Alternatively, the display device may have a digitizer screen (e.g., a touchscreen) that can be touched or otherwise activated with an active or passive digitizer stylus. More alternatively, the control elements described herein may be implemented as a joy stick, touchpad, button pad, group of keyboard arrow keys, mouse, roller ball tracking device, horizontal or vertical rocker-type arm switches, etc., that can be pressed or otherwise moved to actuate the control elements. Alternatively, other forms of entering information can be used, such as textual input (e.g., text boxes) or microphones.

Significantly, during implantation and initial set up of the system 10, patient-specific imaging-related data is generated, and optionally the lead orientation and clinical effects data briefly discussed above. Such patient-specific imaging-related data may include imaging data for the tissue of the patient, such as magnetic resonance imaging (MRI) data, diffusion tensor imaging (DTI) data, and computed tomography (CT) scan data. Additionally or alternatively, the patient-specific imaging-related data may include a transformation data set that is generated using a transformation procedure that transforms a generic 3D atlas into a patient-specific 3D atlas. The transformation data set may also include lead orientation information. The transformation data set may be a 4.times.4 matrix.

Generating the patient-specific imaging-related data may be a complicated procedure, and may require expertise that is only available during initial device implantation and set up. Thus, it is advantageous for the patient-specific imaging-related data to be stored on one or more of the portable components of the system, so that the patient-specific imaging-related data may be easily accessed by external control devices (e.g., a CP 18) during subsequent programming sessions.

As such, the CP 18 is configured for obtaining the patient-specific imaging-related data from the portable component. As discussed in greater detail below, the CP 18 is also configured for generating a patient-specific anatomical atlas from the patient-specific imaging-related data, and for programming the portable component with at least one stimulation parameter based on the patient-specific anatomical atlas.

Having described the arrangement and function of the components within the neurostimulation system 10, one method 200 of storing data patient-specific imaging-related data in one of the portable components of the system 10 will now be described with reference to FIG. 9. This method 200 may be performed by the initial external control device that is used during the implantation and initial follow-up visits.

First, the patient-specific imaging-related data is generated during implantation or initial set up of the system 10 (step 202). For example, imaging data of the tissue of the patient, such as MRI data, DTI data, and/or CT scan data, may be generated.

In another example, a transformation data set may be generated. In a method for generating the transformation data set, MR images of the patient's brain are obtained prior to implanting the IPG 14 and electrodes 26. After the IPG 14 and electrodes 26 are implanted, a CT scan of the patient's brain is obtained in order to show the location of the leads 12 relative to the patient's brain tissue. The CT scan does not show the brain structures as clearly as the MR images. Thus, through a process called "registration," the CT scan information is mapped onto the MR images. This registration process results in a registered MR image that depicts the brain structures as well as the location of the leads 12 within the brain tissue.

After registration, anatomical reference points within the registered MR image are identified. Such anatomical reference points may include the anterior commissure, the posterior commissure, and the mid-sagittal plane. Anatomical reference points may also include coordinates related to the lead position, such as the lead shaft location and/or the lead tip location. Corresponding coordinates are also identified in a generic brain atlas. For example, the anterior commissure, the posterior commissure, and the mid-sagittal plane are identified in the generic brain atlas. Using the points identified in the MR images of the patient's brain and the points identified in the generic brain atlas, a process called "transformation" is performed in order to transform the generic brain atlas into a patient-specific brain atlas. The output of the transformation is a transformation data set. The registration and transformation procedures may be performed by the initial external control device. Further, the registration and transformation procedures may require user expertise beyond that which is required during subsequent follow-up programming sessions.

The size of the transformation data set is dependent upon the complexity of the transformation procedure. For example, if a tri-linear transformation procedure is performed based on the three anatomical points mentioned above, the resulting transformation data set will be a 4.times.4 matrix. If the transformation procedure is based on several points, the resulting transformation data set will be much larger. For example, the transformation data set may be an 8.times.8.times.8 matrix.

After the patient-specific imaging-related data (e.g., imaging data of the tissue of the patient or transformation data set) is generated (step 202), it is stored on one or more of the portable components of the system 10 (step 204), e.g., by transmitting the patient-specific imaging-related data from the external control device to one or more of the portable components. For example, the patient-specific imaging-related data may transmitted to and stored in the memory 48 of the IPG 14 via the telemetry circuitry 50 (shown in FIGS. 2 and 3), in the memory 116 of the RC 16 via the telemetry circuitry 118 (shown in FIG. 7), and/or the memory (not shown) of the external charger 22. Presumably, these steps are performed during an OR mapping session, or initial follow-up session, so that subsequent CPs are capable of interrogating the one or more portable components for the patient-specific imaging-related data.

One of the advantages of storing the patient-specific imaging-related data in the portable component of the system 10 is that the patient-specific imaging-related data will be readily available during follow-up programming sessions, since the portable component or components will presumably be implanted within, worn by, or otherwise carried by the patient. A method 220 of programming the IPG 14 will now be described with reference to FIG. 10. The programming method 220 may be performed by an external control device (e.g., the CP 18) that is different from the initial external control device.

First, the patient-specific imaging-related data, and optionally the lead orientation and clinical effects data, is acquired from the portable component (e.g., from the memory 48 of the IPG 14 (shown in FIGS. 2 and 3), memory 116 of the RC 16 (shown in FIG. 7), and/or the memory (not shown) of the external charger 22 (step 222). Next, a simulation is performed in which a volume of tissue activation is simulated for each of one or more candidate stimulation parameters (step 224). The simulation is based at least in part on the patient-specific imaging-related data and the optional lead orientation and clinical effects data. For example, if the patient-specific imaging-related data is imaging data for the tissue of the patient (e.g., MRI data, DTI data, and/or CT scan data), the external controller may use the imaging data for the tissue of the patient to perform a transformation procedure, as described above. The patient-specific atlas generated from the transformation procedure is used to approximate a volume of the patient's tissue that may be activated for a plurality of different candidate stimulation parameters.

If the patient-specific imaging-related data is the transformation data set, a patient-specific atlas may be generated by simply applying the transformation data set to a generic atlas. The CP 18 comprises a processor configured to simulate a volume of tissue activation. The CP 18 further comprises a display device configured to display the simulated volume of tissue activation relative to the patient-specific atlas. For example, the volume of tissue activation may be superimposed over the patient-specific atlas. Further details describing the simulation methods are disclosed in U.S. Patent Application Publication No. 2012/0165898, entitled "Neurostimulation System For Selectively Estimating Volume Of Activation And Providing Therapy," the disclosure of which is expressly incorporated herein by reference.

After the simulation is performed, at least one of the tested candidate stimulation parameters is selected (step 226). The selected candidate stimulation parameter set(s) can then be programmed into the IPG 14 and/or RC 16 by transmitting and storing the candidate stimulation parameter set(s) in the memory 46 in the IPG 14 and/or the memory 116 of the RC 16 (step 228).

Although particular embodiments of the present inventions have been shown and described, it will be understood that it is not intended to limit the present inventions to the preferred embodiments, and it will be obvious to those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the present inventions. Thus, the present inventions are intended to cover alternatives, modifications, and equivalents, which may be included within the spirit and scope of the present inventions as defined by the claims.

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References


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